Rip currents cause over 100 drowning deaths and more than 30,000 rescues annually in the United States, posing a severe threat to beach safety worldwide. However, most existing detection methods are reactive, identifying rip currents only after they form, leaving limited time for intervention. We propose RipAlert, a future-frame-aware framework that forecasts near-future coastal dynamics and proactively identifies rip current risks. We design a region-sensitive optical flow prediction method with a novel entropy-based object detector to capture early-stage reverse-flow anomalies. Unlike static-image approaches, RipAlert leverages temporal motion patterns to detect rip currents up to 5 seconds before they visibly form. To support real-world deployment, we design a lightweight mobile application and release a curated dataset with over 2,000 annotated images. Experiments on the RipVIS benchmark show that our approach achieves state-of-the-art performance. The system has been deployed at high-risk beaches in China, issuing successful early warnings over real-world events. Our work advances AI-driven coastal safety and contributes to SDG 3 (Good Health and Well-Being) and SDG 13 (Climate Action).

Bolt defect detection is critical to ensure the safety of transmission lines. However, the scarcity of defect images and imbalanced data distributions significantly limit detection performance. To address this problem, we propose a segmentationdriven bolt defect editing method (SBDE) to augment the dataset. First, a bolt attribute segmentation model (Bolt-SAM) is proposed, which enhances the segmentation of complex bolt attributes through the CLAHE-FFT Adapter (CFA) and Multipart- Aware Mask Decoder (MAMD), generating high-quality masks for subsequent editing tasks. Second, a mask optimization module (MOD) is designed and integrated with the image inpainting model (LaMa) to construct the bolt defect attribute editing model (MOD-LaMa), which converts normal bolts into defective ones through attribute editing. Finally, an editing recovery augmentation (ERA) strategy is proposed to recover and put the edited defect bolts back into the original inspection scenes and expand the defect detection dataset. We constructed multiple bolt datasets and conducted extensive experiments. Experimental results demonstrate that the bolt defect images generated by SBDE significantly outperform state-of-the-art image editing models, and effectively improve the performance of bolt defect detection, which fully verifies the effectiveness and application potential of the proposed method. The code of the project is available at https://github.com/Jay-xyj/SBDE.

Anti-vibration hammer defects pose a significant risk to the safe operation of power transmission lines. Addressing the issues posed by the various manifestations of identical category defects and the similarities among different defects, this paper introduces a defect detection algorithm for anti-vibration hammers based on structural knowledge representation. Initially, a Structural Knowledge Enhancement (StKE) Module is proposed to conduct a statistical analysis of the aspect ratios of different defects in anti-vibration hammers, effectively extracting the structural features of the anti-vibration hammers and their corresponding defects. Subsequently, a Structural Knowledge Representation (StKR) Module is introduced, which bolsters the model’s ability to precisely locate defects that disrupt structural symmetry. The model incorporates the Coordinate Attention (CA) mechanism to acquire contextual information, thereby enhancing detection accuracy. Experimental results demonstrate that the improved model achieves a 6.1% increase in mean detection precision over the baseline model, and a notable improvement in detection accuracy compared to other advanced algorithms.

With the advancement of high-resolution aerial imaging technology enabled by unmanned aerial vehicles (UAVs), insulator defect detection based on images has emerged as a key approach for intelligent inspection of overhead transmission lines. However, the insulator defective region detection (IDRD) task based on such imagery continues to face several critical challenges, including complex background interference, difficulty in detecting small-scale defects, and class imbalance in sample distribution. To tackle these challenges, we propose a state space model guided deep feature perception network for insulator defect detection (SGFP-YOLO). Specifically, the model introduces a Bi-directional Feature Enhancement Module (BFEM) and a Dual-context Feature Refinement Module (DFRM), both guided by a state-space framework, to enhance global feature and capture local detail features. Additionally, to address the hard-easy sample imbalance problem in the detection process, we introduce Focused Balance Loss, improving the performance of model in both classification and regression tasks. Experiments are conducted on the self-constructed TLID dataset and the publicly available IDID dataset. The results show that SGFP-YOLO outperforms existing advanced models in several metrics, including [email protected] and F1-score, especially in complex background and small-scale defect detection, providing an efficient and accurate solution for intelligent detection of insulators in transmission line.

Edge detection is frequently employed to support downstream visual tasks. However, current edge detection methods still encounter two significant challenges: extracting complex textured targets and capturing valuable information from complex backgrounds. We propose FFED, a flow field-guided edge detection model. FFED integrates the three components of our design. FFED incorporates three designed components: the Feature Broadcast Module (FBM), the Antagonistic Bio-inspired Spatial Attention Module (ABSAM), a novel pixel difference convolution named ALS. The FBM serves as an implementation mode of the flow field, with its input pair selection strategy inspired by video processing. The FBM broadcasts high-level semantic features to high-resolution ones, preserving more meaningful texture details. Inspired by biological studies, we propose the ABSAM. ABSAM extracts valuable information from complex backgrounds by optimizing spatial modeling of data. The ALS exhibits enhanced capability in extracting gradient information and capturing subtle texture details that are easily overlooked. Experimental results demonstrate that FFED achieved competitive detection results on NYUD, BSDS500, and BIPED datasets, as well as good performance on industrial datasets. Additionally, the experiment verified the auxiliary effect of FFED on downstream visual tasks. The code is available at https://github.com/hanyuchen2022/Flow-field-guided-edge-detection-FFED-.

With the rapid development of high-resolution satellite remote sensing observation technology, power tower detection based on satellite remote sensing images has become a key research focus for power intelligent inspection. However, the performance of power tower detection in satellite remote sensing images needs improvement due to complex back-grounds, small and non-uniform target sizes. To address this, this paper first constructs a multi-scene high-resolution satellite remote sensing power tower dataset, and then proposes the LSKF-YOLO network for high-resolution satellite remote sensing images. This network primarily consists of a large spatial kernel selective attention fusion module and a multi-scale feature alignment fusion structure. The large spatial selective kernel mechanism is improved by using the attentional feature fusion module, provides richer feature information for accurately locating the position of the power tower. The multi-scale feature alignment fusion structure effectively utilizes low-level semantic information, mitigates feature ambiguity in deeper network layers, and enables multi-scale feature fusion of power towers within complex backgrounds. Additionally, the introduction of MPDIoU enhances CIoU, further improving the model’s performance. The results demonstrate that the F1 score and mAP0.5 of the LSKF-YOLO network reach 0.764 and 77.47%, respectively. Compared with other deep learning-based satellite remote sensing power tower inspection methods, the LSKF-YOLO network significantly enhances detection accuracy and provides crucial technical support for intelligent inspection of power lines via satellite remote sensing.

Age estimation from a single face image has been an essential task in the field of human-computer interaction and computer vision, which has a wide range of practical application values. Accuracy of age estimation of face images in the wild is relatively low for existing methods, because they only take into account the global features, while neglecting the fine-grained features of age-sensitive areas. We propose a novel method based on our attention long short-term memory (AL) network for fine-grained age estimation in the wild, inspired by the fine-grained categories and the visual attention mechanism. This method combines the residual networks (ResNets) or the residual network of residual network (RoR) models with LSTM units to construct AL-ResNets or AL-RoR networks to extract local features of age-sensitive regions, which effectively improves the age estimation accuracy. First, a ResNets or a RoR model pretrained on ImageNet dataset is selected as the basic model, which is then fine-tuned on the IMDB-WIKI-101 dataset for age estimation. Then, we fine-tune the ResNets or the RoR on the target age datasets to extract the global features of face images. To extract the local features of age-sensitive regions, the LSTM unit is then presented to obtain the coordinates of the age-sensitive region automatically. Finally, the age group classification is conducted directly on the Adience dataset, and age-regression experiments are performed by the Deep EXpectation algorithm (DEX) on MORPH Album 2, FG-NET and 15/16LAP datasets. By combining the global and the local features, we obtain our final prediction results. Experimental results illustrate the effectiveness and robustness of the proposed AL-ResNets or AL-RoR for age estimation in the wild, where it achieves better state-of-the-art performance than all other convolutional neural network (CNN) methods on the Adience, MORPH Album 2, FG-NET and 15/16LAP datasets.

Recent network research has demonstrated that the performance of convolutional neural networks can be improved by introducing a learning block that capture spatial correlations and channel-wise correlations . In this work, we propose a novel Channel-wise and Feature-points Reweights DenseNet (CAPR-DenseNet) architecture. The CAPR-DenseNet improves the representation power of the DenseNet by adaptively recalibrating the channel-wise feature responses and explicitly modeling the interdependencies between feature-points. First, in order to perform dynamic channel-wise feature recalibration, we construct the Channel-wise Feature Reweight DenseNet (CFR-DenseNet) by introducing the Squeeze-and-Excitation Module (SEM) to DenseNet. Then, we present a novel CAPR-DenseNet by adding a Feature-points Reweight Module (FPRM) to the CFR-DenseNet. Through massive experiments, we demonstrate that by recalibrating the channel-wise feature and the feature-points responses. Our CAPR-DenseNet performs better than DenseNet across challenging datasets CIFAR-10 and CIFAR-100.

Cross-entropy loss function (CEL) is widely used for training a multi-class classification deep convolutional neural network (DCNN). While CEL has been successfully implemented in image classification tasks, it only focuses on the posterior probability of correct class when the labels of training images are one-hot. It cannot be discriminated against the classes not belong to correct class (wrong classes) directly. Negative Log Likelihood Ratio Loss (NLLR) is proposed to better discriminate the correct class from competing wrong classes. But optimization of the loss function is normally presented as a minimization problem. In training DCNN, the value of NLLR is not constantly positive or negative, which affects the convergence of NLLR adversely. So, we propose competing ratio loss (CRL), which calculates the posterior probability ratio between the correct class and competing wrong classes to better widen the difference between the probability of the correct class and the probabilities of wrong classes, which also assures the value of CRL is constantly positive. Through massive experiments, we demonstrate the effectiveness and robustness of CRL on deep convolutional neural networks, our CRL outperforms CEL and NLLR on CIFAR-10/100 datasets.

Age estimation from a single face image has been an essential task in the field of human-computer interaction and computer vision which has a wide range of practical application value. Concerning the problem that accuracy of age estimation of face images under unconstrained conditions are relatively low for existing methods, we propose a method based on Attention LSTM network for Fine-Grained age group classification in the wild based on the idea of Fine-Grained categories and visual attention. This method combines ResNets models with LSTM unit to construct AL-ResNets networks to extract age-sensitive local regions, which effectively improves age estimation accuracy. Firstly, ResNets model pre-trained on ImageNet data set is selected as the basic model, which is then fine-tuned on the IMDB-WIKI-101 data set for age estimation. Then, we fine-tune ResNets on the Adience data set to extract the global features of face images. To extract the local characteristics of age-sensitive areas, the LSTM unit is then presented to obtain the coordinates of the age-sensitive region automatically. Finally, by combining the global and local features, we got our final prediction results. Our experiments illustrate the effectiveness of AL-ResNets for age group classification in the wild, where it achieves new state-of-the-art performance than all other CNN methods on the Adience data set.

Automatically predicting age group from face images acquired in unconstrained conditions is an important and challenging task in many real-world applications. Nevertheless, the conventional methods with manually-designed features on in-the-wild benchmarks are unsatisfactory because of incompetency to tackle large variations in unconstrained images. In this paper, we propose a new CNN based method for age group classification leveraging Residual Networks of Residual Networks (RoR), which exhibits better optimization ability for age group classification than other CNN architectures. Moreover, two modest mechanisms based on observation of the characteristics of age group are presented to further improve the performance of age estimation. Our experiments illustrate the effectiveness of RoR method for age estimation in the wild, where it achieves better performance than other CNN methods. Finally, the Pre-RoR-58+SD with two mechanisms achieves new state-of-the-art results on Adience benchmark.

The status of the insulators in power line can directly affect the reliability of the power transmission systems. Computer vision aided approaches have been widely applied in electric power systems. Inspecting the status of insulators from aerial images has been challenging due to the complex background and rapid view changing under different illumination conditions. In this paper, we propose a novel approach to inspect the insulators with Deep Convolutional Neural Networks (CNN). A CNN model with multi-patch feature extraction method is applied to represent the status of insulators and a Support Vector Machine (SVM) is trained based on these features. A thorough evaluation is conducted on our insulator status dataset of six classes from real inspection videos. The experimental results show that a pre-trained model for classification is more accurate than the shallow features by hand-crafted. Our approach achieves 98.7095% mean Average Precision (mAP) in status classification. We also study the behavior of the neural activations of the convolutional layers. Different results vary with different fully connected layers, and interesting findings are discussed.