With advances in microbiomics, the crucial role of microbes in disease progression is increasingly recognized. However, predicting disease phenotypes using microbiome data remains challenging due to data complexity, heterogeneity, and limited model generalization. Current methods often depend on specific datasets and are vulnerable to adversarial attacks. To address these issues, this paper introduces a novel Noise Perturbation Ensemble Neural Network model (NPENN), which combines noise mechanisms with Gradient Boosting (GB) techniques for robust neural network ensemble learning. NPENN, validated on multiple microbiome datasets, shows superior accuracy and generalization compared to traditional methods, effectively handling data complexity and variability. This approach enhances model robustness and feature learning by integrating GB prior knowledge. Additionally, the study explores microbial community roles in various diseases, providing insights into disease mechanisms and potential biomarkers for personalized precision diagnosis and treatment strategies.

Current LiDAR-only 3D detection methods are limited by the sparsity of point clouds. The previous method used pseudo points generated by depth completion to supplement the LiDAR point cloud, but the pseudo points sampling process was complex, and the distribution of pseudo points was uneven. Meanwhile, due to the imprecision of depth completion, the pseudo points suffer from noise and local structural ambiguity, which limit the further improvement of detection accuracy. This paper presents SQDNet, a novel framework designed to address these challenges. SQDNet incorporates two key components: the SQD, which achieves sparse-to-dense matching via grid position indices, allowing for rapid sampling of large-scale pseudo points on the dense depth map directly, thus streamlining the data preprocessing pipeline. And use the density of LiDAR points within these grids to alleviate the uneven distribution and noise problems of pseudo points. Meanwhile, the sparse 3D Backbone is designed to capture long-distance dependencies, thereby improving voxel feature extraction and mitigating local structural blur in pseudo points. The experimental results validate the effectiveness of SQD and achieve considerable detection performance for difficult-to-detect instances on the KITTI test.

Traffic Light Recognition (TLR) aims at detecting Traffic Lights (TLs) and then classifying the status of light signals, being an essential constituent of autonomous driving perception systems. However, it’s challenging for existing TLR methods to accurately distinguish both color and shape status of TLs due to small object sizes, illumination variations, close resemblance with other objects and varying weather conditions. Existing public datasets for TLR have three main drawbacks:(i) poor diversity, (ii) sample imbalance, and (iii) insufficient category labels, greatly hindering the development of TLR. To overcome the aforementioned problems, we propose a Robust Traffic Light Recognition Pipeline based on YOLOv8 (RTLRP-YOLO) that can recognize TLs accurately with strong robustness based on adaptively generated high-quality images. Specifically, we develop a Self-Adaptive Preprocessing Module (SAPM) which is designed to adaptively generate high-quality images under hostile conditions, followed by a Two-stage Traffic Light Recognition Model based on YOLOv8 (TTRM) to obtain both the location and status information of TLs. Moreover, We also provide our self-made Tongji Small Traffic Light Dataset (TSTLD), covering a variety of weather conditions, regions, light intensities and shooting angles. To the best of our knowledge, our proposed method is the first one to be able of simultaneously identifying three colors (i.e., red, yellow and green) and four shapes (i.e., circle, left arrow, right arrow and up arrow) of TLs, achieving 95.43% accuracy on TSTLD with the inference time of 26 ms for per image.

Vision-based road friction coefficient estimation received extensive attention in the field of road maintenance and autonomous driving. However, the current mainstream coarse-grained friction estimation methods are basically based on image classification tasks. This makes it difficult to deal with complex road conditions in changing weathers. Many models can correctly predict the friction coefficients of the road as a whole in consistent and simple road conditions, but perform poorly otherwise. The existing image benchmarks in this field rarely consider the above problems as well, which limits the comparable evaluations of different models. Therefore, in this paper, we first construct a challenging pixel-level friction coefficient estimation dataset WRF-P to evaluate model performances under mixed road conditions. Then, we propose a friction coefficient estimation method based on weakly supervised learning and uncertainty estimation to realize pixel-level road friction prediction with low annotation cost. The model outperforms existing weakly supervised methods and reaches 39.63% mIOU on the WRF-P dataset. The WRF-P dataset will be made publicly available at https://github.com/blackholeLFL/The-WRF-dataset soon.

Upsampling operation is necessary for semantic segmentation and other pixel-level prediction tasks. Among the commonly used upsampling operations, some are too simple to effectively recover the spatial details lost during downsampling process, and some are too complex and have high computation complexity. In real-world applications, it is critical to achieve high accuracy and maintain real-time inference speed. Therefore, an efficient upsampling operation is essential for these tasks. In this paper, we introduce efficient adaptive upsampling module (EAUM) for real-time semantic segmentation. Inspired by dynamic filter networks, EAUM adaptively predicts the kernel weight of each point in the upsampled feature map according to the corresponding points in the input feature map. To reduce computational cost, EAUM decomposes the spatial information and channel information required for upsampling. The proposed EAUM shows impressive performance on Cityscapes and CamVid benchmarks. Specifically, DenseENet with EAUM outperforms the baseline by 1.4% [Formula: see text] and 1.6% [Formula: see text] in accuracy with a slight drop in inference speed on Cityscapes test dataset.

Real-time semantic segmentation is a challenging task as both accuracy and inference speed need to be considered simultaneously. In real-world applications, it is usually achieved by deploying a deep neural network in modern GPU device. However, most of the work focused on real-time semantic segmentation is designed by significantly reducing computation complexity and model size. There are other factors that have a significant impact on inference speed are overlooked, especially when the network is running in modern GPU device. In this paper, we focus on designing a GPU-efficient network as backbone for real-time semantic segmentation. Dense connectivity can preserve and accumulate feature maps of multiple receptive fields and is therefore ideal for semantic segmentation. Therefore, we design a GPU-efficient network (DenseENet) with dense connectivity. The proposed DenseENet shows an obvious advantage in balancing accuracy and inference speed in modern GPU device. Specifically, on Cityscapes test set, DenseENet with a simple FCN decoder achieves 75.2% mIoU with 83.6 FPS for an input of 1024 × 2048 resolution and 73.6% mIoU with 132 FPS for an input of 768 × 1536 resolution on a single GTX 1080Ti card.

For the domain of self-driving and automatic parking, perception is a basic and critical technique, moreover, the detection of lane markings and parking slots is an important part of visual perception. Compared with front sight images, panoramic images(PI) can capture more comprehensive pavement information. However, the imbalance of different classes in PI is even more serious. Additionally, the judgment of boundary information between areas is a hard problem in deep models. Therefore, we propose a new model named DFNet to solve these problems. The proposed model has two main contributions, one is dynamic loss weights, and the other is residual fusion block(RFB). DFNet use dynamic loss weights to overcome the negative effect of imbalance dataset, which are calculated according to the pixel number of each class in a batch. RFB is composed of several convolutional layers, a pooling layer, and a fusion layer to combine the feature maps by pixel multiplication, which can reduce boundary information loss. We evaluate our method on PSV dataset, and the achieved advanced results demonstrate the effectiveness of the proposed model.

Intelligent vehicles need to detect new classes of traffic objects while keeping the performance of old ones. Deep convolution neural network (DCNN) based detector has shown superior performance, however, DCNN is ill-equipped for incremental learning, i.e., a DCNN based vehicle detector trained on traffic sign dataset will catastrophic forget how to detect vehicles. In this paper, we propose a novel method to alleviate this problem, our key insight is that the original class of objects also appears in new task data, by utilizing these objects, our method effectively keeps the detection accuracy of original models while incremental learning to detect new classes of objects. Detailed experiments on PASCAL VOC dataset and TSD-max database verified the effectiveness of our method.

The automatic parking is being massively developed by car manufacturers and providers. Until now, there are two problems with the automatic parking. First, there is no openly-available segmentation labels of parking slot on panoramic surround view (PSV) dataset. Second, how to detect parking slot and road structure robustly. Therefore, in this paper, we build up a public PSV dataset. At the same time, we proposed a highly fused convolutional network (HFCN) based segmentation method for parking slot and lane markings based on the PSV dataset. A surround-view image is made of four calibrated images captured from four fisheye cameras. We collect and label more than 4,200 surround view images for this task, which contain various illuminated scenes of different types of parking slots. A VH-HFCN network is proposed, which adopts an HFCN as the base, with an extra efficient VH-stage for better segmenting various markings. The VH-stage consists of two independent linear convolution paths with vertical and horizontal convolution kernels respectively. This modification enables the network to robustly and precisely extract linear features. We evaluated our model on the PSV dataset and the results showed outstanding performance in ground markings segmentation. Based on the segmented markings, parking slots and lanes are acquired by skeletonization, hough line transform and line arrangement.

With the rapid development of driverless cars, pedestrian detection has been a canonical instance of object detection. Although recent deep learning detectors such as RPN+BF and MS-CNN have shown excellent performance for pedestrian detection, they have limited success for detecting pedestrian, and the importance of final feature receptive field has been awared by previous leading deep learning pedestrian detectors. Applying the dilated convolution to the feature learning of pedestrian detection, we constructed a pedestrian detection framework along with the region proposal network and boosted decision trees. Pipeline of our proposed framework can be briefly generalized as follows: firstly, the fine-tuned RPN with specified aspect ratio is used to get boxes and scores. Secondly, the designed dilated convolution feature extraction model is used to get features. As different dilation factors provide different receptive field scales, we concat the features of different layers with the dilated convolutional features to get the final features. Finally, the candidate boxes are sent to the boosted decision trees to be classified using the scores and features. We evaluated our method on the Caltech Pedestrian Detection Benchmark. Comparing with other state-of-the-art detection methods, the proposed framework with dilated convolution has better performance.

RGB-D-based object recognition has been enthusiastically investigated in the past few years. RGB and depth images provide useful and complementary information. Fusing RGB and depth features can significantly increase the accuracy of object recognition. However, previous works just simply take the depth image as the fourth channel of the RGB image and concatenate the RGB and depth features, ignoring the different power of RGB and depth information for different objects. In this paper, a new method which contains three different classifiers is proposed to fuse features extracted from RGB image and depth image for RGB-D-based object recognition. Firstly, a RGB classifier and a depth classifier are trained by cross-validation to get the accuracy difference between RGB and depth features for each object. Then a variant RGB-D classifier is trained with different initialization parameters for each class according to the accuracy difference. The variant RGB-D-classifier can result in a more robust classification performance. The proposed method is evaluated on two benchmark RGB-D datasets. Compared with previous methods, ours achieves comparable performance with the state-of-the-art method.

In recent years, deep learning approaches have been successfully used to learn hierarchical representations of image data, audio data etc. However, to our knowledge, these deep learning approaches have not been extensively studied for electroencephalographic (EEG) data. Considering the properties of EEG data, high-dimensional and multichannel, we applied convolutional deep belief networks to the feature learning of EEG data and evaluated it on the datasets from previous BCI competitions. Compared with other state-of-the-art feature extraction methods, the learned features using convolutional deep belief network have better performance.