Unordered grasping in industrial robotic manipulation requires precise six-degree-of-freedom (6D) pose estimation. However, existing methods often struggle with unknown objects and require retraining, limiting their practicality. Traditional 3D point-pair feature methods, while training-free, perform poorly with textured symmetric objects. We propose a generalizable approach for zero-shot 6 D pose estimation without retraining. Our method consists of two steps: generating CAD-based templates through real-time rendering for coarse pose estimation, and refining poses using semantic point-pair features aligned with the camera viewpoint. We conducted experiments on seven core datasets from the Benchmark for 6D Object Pose Estimation (BOP) challenge, and the results are publicly available on the BOP website. Integration into a robotic grasping system further highlights its high precision and fast execution, making it ideal for applications such as bin-picking. (GZS6D-BP) https://bop.felk.cvut.cz/leaderboards/.

Household chores are an essential part of daily life. Household chores include cleaning, laundry, cooking, and many others, but it is difficult to perform them every day. In particular, cleaning grease stains is often stubborn and troublesome. In addition, many ventilation fans are complicated in shape and difficult to wash. Among exhaust fans, sirocco fans are more difficult to clean than propeller fans. It takes about an hour of hard labor to scrub them with a brush, bending over at the waist. To solve this problem, we propose a system that automatically cleans the sirocco fan of a ventilation fan by simply setting it in place. The fan of the ventilation fan is soaked in water dissolved with detergent or baking soda and fixed in the bathtub, and a brush attached to the motor brushes the spaces between the fan to remove dirt. The system then takes before and after pictures of the fan before and after cleaning, and only the fan is extracted and compared to see how much dirt has been removed.

6D pose estimation of arbitrary objects is a crucial topic for intelligent transportation infrastructure measurement. However, some external environmental factors and the characteristics of the object itself impact the accuracy of the object’s pose estimation in practical applications. In this paper, we propose a new multi-class dataset ICD-4 (Industrial car Components Dataset) for 6D object pose estimation, which mainly includes four component categories, and every category takes 20,000 different scenarios. ICD-4 dataset delivers quite a few research challenges involving the range of object pose transformations and has significant research value for small-scale pose estimation tasks. We also propose an innovative method PoseMLP, a pose estimation network that uses residual MLP (multilayer perceptron) modules to predict the 6D pose estimation directly. Simultaneously, the experimental results demonstrate the effectiveness and reliability of the proposed method.

In the process of transportation, the handling and loading methods of rigid objects are becoming more and more perfect. However, whether in today’s transportation system or in daily life, such as packing objects or sorting cables before transportation, the manipulation of deformable objects has been always inevitable and has attracted more and more attention. Due to the super degrees of freedom and the unpredictable physical state of deformed objects. It is difficult for robots to complete tasks under the environment of the deformable object. Therefore, we present a method based on imitation learning. In the generated expert demonstration, the agent is offered to learn the state sequence, and then imitate the expert’s trajectory sequence which avoid the above-mentioned difficulties. In addition, compared with the baseline method, our proposed DN-Transporter Networks are more competitive in a simulation environment involving cloth, ropes or bags.

In recent years, robots have been used not only in factories. However, most robots currently used in such places can only perform the actions programmed to perform in a predefined space. For robots to become widespread in the future, not only in factories, distribution warehouses, and other places but also in homes and other environments where robots receive complex commands and their surroundings are constantly being updated, it is necessary to make robots intelligent. Therefore, this study proposed a deep learning grasp position estimation model using depth images to achieve intelligence in pick-and-place. This study used only depth images as the training data to build the deep learning model. Some previous studies have used RGB images and depth images. However, in this study, we used only depth images as training data because we expect the inference to be based on the object's shape, independent of the color information of the object. By performing inference based on the target object's shape, the deep learning model is expected to minimize the need for re-training when the target object package changes in the production line since it is not dependent on the RGB image. In this study, we propose a deep learning model that focuses on the stacked encoder-decoder structure of the Stacked Hourglass Network. We compared the proposed method with the baseline method in the same evaluation metrics and a real robot, which shows higher accuracy than other methods in previous studies.

Vehicle logo detection (VLD) is a special and significant topic in object detection for vehicle identification system applications. Nevertheless, the range of the research and analysis for VLD are seriously narrow in the real complex scenes, although it’s a critical role in the object detection of small sizes. In this paper, we make further analysis work toward vehicle logo recognition and detection in real-world situations. To begin with, we propose a new multi-class VLD dataset, called VLD-45 (Vehicle Logo Dataset), which contains 45000 images and 50359 objects from 45 categories respectively. Our new dataset provides several research challenges involve in small sizes object, shape deformation, low contrast and so on. Meanwhile, we use 6 existing classifiers and 6 detectors to evaluate our dataset and show the baseline performance. According to the result, our dataset has very significant research value for the task of small-scale object detection. The dataset source:https://github.com/YangShuoys/VLD-45-B-DATASET-Detection

Industrial Internet of Things (IIoT) provide significant support for observing and controlling industrial machinery. In this article, a novel hierarchical feature enhancement network (HFEN) is proposed by combining signal processing and representation learning. The signal processing block extracts features with definite physical significance. Then, the representability of the physical features is improved by connecting stacked denoising autoencoders and squeeze-and-excitation networks. A novel two-stream architecture is designed for HFEN to fuse two types of features. Consequently, HFEN can extract features that can be analyzed for physical significance and that are also representative in terms of recognizable patterns. The experimental results prove that the performance of HFEN is satisfactory in terms of accuracy and efficiency when compared to other methods. Finally, this article also aims to demonstrate the potential of a new pairing that fuses the model- and data-driven strategies for IIoT.

Big data-driven deep learning methods have been widely used in image or video segmentation. The main challenge is that a large amount of labeled data is required in training deep learning models, which is important in real-world applications. To the best of our knowledge, there exist few researches in the deep learning-based visual segmentation. To this end, this paper summarizes the algorithms and current situation of image or video segmentation technologies based on deep learning and point out the future trends. The characteristics of segmentation that based on semi-supervised or unsupervised learning, all of the recent novel methods are summarized in this paper. The principle, advantages and disadvantages of each algorithms are also compared and analyzed.

Unmanned aerial vehicles (UAVs) are used in many fields including weather observation, farming, infrastructure inspection, and monitoring of disaster areas. However, the currently available UAVs are prone to crashing. The goal of this paper is the development of an anomaly detection system to prevent the motor of the drone from operating at abnormal temperatures. In this anomaly detection system, the temperature of the motor is recorded using DS18B20 sensors. Then, using reinforcement learning, the motor is judged to be operating abnormally by a Raspberry Pi processing unit. A specially built user interface allows the activity of the Raspberry Pi to be tracked on a Tablet for observation purposes. The proposed system provides the ability to land a drone when the motor temperature exceeds an automatically generated threshold. The experimental results confirm that the proposed system can safely control the drone using information obtained from temperature sensors attached to the motor.

Summary Wound area changes over multiple weeks are highly predictive of the wound healing process. A big data eHealth system would be very helpful in evaluating these changes. We usually analyze images of the wound bed for diagnosing injury. Unfortunately, accurate measurements of wound region changes from images are difficult. Many factors affect the quality of images, such as intensity inhomogeneity and color distortion. To this end, we propose a fast level set model‐based method for intensity inhomogeneity correction and a spectral properties‐based color correction method to overcome these obstacles. State‐of‐the‐art level set methods can segment objects well. However, such methods are time‐consuming and inefficient. In contrast to conventional approaches, the proposed model integrates a new signed energy force function that can detect contours at weak or blurred edges efficiently. It ensures the smoothness of the level set function and reduces the computational complexity of re‐initialization. To increase the speed of the algorithm further, we also include an additive operator‐splitting algorithm in our fast level set model. In addition, we consider using a camera, lighting, and spectral properties to recover the actual color. Numerical synthetic and real‐world images demonstrate the advantages of the proposed method over state‐of‐the‐art methods. Experimental results also show that the proposed model is at least twice as fast as methods used widely. Copyright © 2016 John Wiley & Sons, Ltd.

Vision-based underwater navigation and object detection requires robust computer vision algorithms to operate in turbid water. Many conventional methods aimed at improving visibility in low turbid water. In this paper, we propose a novel contrast enhancement to enhance high turbid underwater images using descattering and color correction. The proposed enhancement method removes the scatter and preserves colors. In addition, as a rule to compare the performance of different image enhancement algorithms, a more comprehensive image quality assessment index Qu is proposed. The index combines the benefits of SSIM index and color distance index. Experimental results show that the proposed approach statistically outperforms state-of-the-art general purpose underwater image contrast enhancement algorithms. The experiment also demonstrated that the proposed method performs well for image classification.

In recent years, sonar has been widely used for restoring the underwater terrain. Sonar imaging has the benefits such as long-range photographing, robust for turbidity water. However, it is not suitable for short-range imaging. Meanwhile, it also cannot meet the need of mining machine. Therefore, it is important to develop a 3D reconstruction method for short-range imaging. In this paper, we propose a Kinect-based underwater 3D image reconstruction method. To overcome the drawbacks of low accuracy of depth maps, we propose a novel super-resolution (SR) method, which uses the underwater dark channel prior dehazing, weight guided image SR, and inpainting. The proposed method considered the influence of mud sediments in water, it performs better than the traditional methods. The experimental results demonstrated that, after inpainting, dehazing and the super-resolution, it can obtain high accuracy depth maps.

Absorption, scattering, and color distortion are three major issues in underwater optical imaging. Light rays traveling through water are scattered and absorbed according to their wavelength. Scattering is caused by large suspended particles that degrade optical images captured underwater. Color distortion occurs because different wavelengths are attenuated to different degrees in water; consequently, images of ambient underwater environments are dominated by a bluish tone. In the present paper, we propose a novel underwater imaging model that compensates for the attenuation discrepancy along the propagation path. In addition, we develop a fast weighted guided normalized convolution domain filtering algorithm for enhancing underwater optical images in shallow oceans. The enhanced images are characterized by a reduced noised level, better exposure in dark regions, and improved global contrast, by which the finest details and edges are enhanced significantly.

We present a novel method of enhancing shallow ocean optical images or videos using weighted guided median filter and wavelength properties. Absorption, scattering and color distortion are three major distortion issues for underwater optical imaging. Light rays traveling through water are scattered and absorbed depending on the wavelength. Scattering is caused by large suspended particles, as in turbid water that contains abundant particles, which causes the degradation of the image. Color distortion occurs because different wavelengths are attenuated to different degrees in water, causing ambient underwater environments to be dominated by a bluish tone. Our key contributions are proposed include a novel shallow water imaging model that compensates for the attenuation discrepancy along the propagation path and an effective underwater scene enhancement scheme. The recovered images are characterized by a reduced noised level, better exposure of the dark regions, and improved global contrast where the finest details and edges are enhanced significantly.

As aging society problem goes severe, systems to confirm to safety of elders in daily life are expected to relieve burdensome safety confirmation tasks of care workers. In this paper, a sensor, which detects person localization without privacy offending, applying Obrid-Sensor is proposed. In the proposed design, the Obrid-Sensor is constructed with a line sensor and a slit to obtain one-dimensional brightness distribution. The proposed sensor is able to obtain one-dimensional brightness distribution that is approximately equal to integration value of each vertical pixel line of two-dimensional image. Meanwhile, the novel slit type Obrid-Sensor, which was constructed without Rod lens, is studied in this research. By employing the proposed sensor, the information of a subject's position and motion can be obtained without using two-dimensional texture image. The effectiveness of the proposed method is confirmed by experiments.

This paper describes a novel method to enhance underwater optical images by guided trigonometric bilateral filters and color correction. Scattering and color distortion are two major problems of distortion for underwater optical imaging. Scattering is caused by large suspended particles, like fog or turbid water which contains abundant particles. Color distortion corresponds to the varying degrees of attenuation encountered by light traveling in the water with different wavelengths, rendering ambient underwater environments dominated by a bluish tone. Our key contributions are proposed a new underwater model to compensate the attenuation discrepancy along the propagation path, and to propose a fast guided trigonometric bilateral filtering enhancing algorithm and a novel fast automatic color enhancement algorithm. The enhanced images are characterized by reduced noised level, better exposedness of the dark regions, improved global contrast while the finest details and edges are enhance significantly. In addition, our enhancement method is comparable to higher quality than the state-of-the-art methods by assuming in the latest image evaluation systems.

This paper describes a novel method to enhance underwater optical images by dehazing. Scattering and color change are two major problems of distortion for underwater imaging. Scattering is caused by large suspended particles, like fog or turbid water which contains abundant particles, plankton etc. Color change corresponds to the varying degrees of attenuation encountered by light traveling in the water with different wavelengths, rendering ambient underwater environments dominated by a bluish tone. Our key contribution is to propose a fast image and video dehazing algorithm, to compensate the attenuation discrepancy along the propagation path, and to take the influence of the possible presence of an artificial lighting source into consideration. The enhanced images are characterized by reduced noised level, better exposedness of the dark regions, improved global contrast while the finest details and edges are enhance significantly. In addition, our enhancement method is comparable to higher quality than the state-of-the-art methods.

In this paper we propose a novel explicit image filter called guided depth image filter for natural image matting. Different from the traditional matting model, the guided image filter computes the filtering output by considering the content of a depth image. The guided depth image filter can be used as an edge-preserving smoothing operator like bilateral filter, but has better behaviors near edges. The proposed filter by using nonlocal neighborhoods, and contribute a simple and fast algorithm giving competitive results. Experimental results indicate that our matting results are comparable to the state of the art methods.

Recently, new touch panels are actively developed. And, the device with the touch panel is widespread. However, past touch panels cannot fold. Therefore, they can be set up only on the plane. Flexibility is needed for the touch panel to set up the touch panel in various places. Then, we propose the flexible touch panel sensor. This sensor is made from a resistance films and an insulation film and metallic foils. The place touched can be specified according to the current ratio that flows to the circuit when the person touches the sensor. This sensor can be installed on various places.

This paper proposed a new approach of 3D measurement using a home use 3D camera. Stereo image measurement is different from the other active measurement method like using a laser range finder or an ultra-sonic sensor. It is a passive method, which acquire the object as two images, and then calculate the distance information form the two images according to the principle of triangulation. Ordinarily, such of two images is taken by a special use stereo camera which were settled with a precisely accuracy so that to keep a parallel optical axes, depth of focus and so on. But the accuracy settings of a home use 3D camera cannot satisfy such a requirement. In this paper, phase-only-correlation method which can yield sub-pixel accuracy is used, also with some modification and new approach. The simulation shows a good result.

As a novel of multi-resolution analysis tool, the modified sharp frequency localized contour let transforms (MSFLCT) provides flexible multiresolution, anisotropy, and directional expansion for medical images. In this paper, we proposed a new fusion rule for multimodal medical images based on MSFLCT. The multimodal medical images are decomposed by MSFLCT. For the high-pass sub band, the weighted sum modified laplacian (WSML) method is used for choose the high frequency coefficients. For the low pass sub band, the maximum local energy (MLE) method is combined with "region" idea for low frequency coefficient selection. The final fusion image is obtained by applying inverse MSFLCT to fused low pass and high pass sub bands. Abundant experiments have been made on groups of multimodality datasets, both human visual and quantitative analysis show that the new strategy for attaining image fusion with satisfactory performance.

In this paper, we firstly propose the maximum local energy (MLE) method to calculate the low frequency coefficients of images and compare the results with those of mirror extended curve let transform, which enhance the edge features and details of images. An image fusion step was performed as follows: First, we obtained the coefficients of two different types of images through mirror extended curve let transform. Second, we selected the low frequency coefficients by maximum local energy and obtaining the high-frequency coefficients using the absolute maximum value (AMV) method. Finally, the fused image was obtained by performing an inverse mirror extended curve let transform. In addition to human vision analysis, the images were also compared through quantitative analysis. multifocus images were used in the experiments to compare the results among the beyond wavelets. The numerical experiments reveal that maximum local energy is a new strategy for attaining image fusion with satisfactory performance.