With the proposed goal of “Carbon Neutrality”, photovoltaic energy is gradually gaining the leading role in energy transformation. At present, crystalline silicon cells are still the mainstream technology in the photovoltaic industry, but due to the similarity of defect characteristics and the small scale of the defects, automatic defect detection of photovoltaic cells (PV) by electroluminescence (EL) imaging is a challenging task. In order to better meet the growing demand for high-quality photovoltaic cell products in intelligent manufacturing and use, and ensure the safe and efficient operation of photovoltaic power stations, this paper proposes an improved abnormal detection method based on Faster R-CNN for the surface defect EL imaging of photovoltaic cells, which integrates a lightweight channel and spatial convolution attention module. It can analyze the crack defects in complex scenes more efficiently. The clustering algorithm was used to obtain a more targeted anchor frame for photovoltaic cells, which made the model converge faster and enhanced the detection ability. The normalized distance between the prediction box and the target box is minimized by considering the DIoU loss function for the overlapping area of the boundary box and the distance between the center points. The experiment shows that the average accuracy of surface defect detection for EL images of photovoltaic cells is improved by 14.87% compared with the original algorithm, which significantly improves the accuracy of defect detection. The model can better detect small target defects, meet the requirements of surface defect detection of photovoltaic cells, and proves that it has good application prospects in the field of photovoltaic cell defect detection.

Object detection plays a crucial role in the fields of autonomous driving, security surveillance, UAVs, etc. However, the performance of detectors can be drastically degraded by adverse weather conditions such as fog, rain, and snow. This is because detectors are usually trained on images taken in clear weather conditions but tested under adverse weather conditions. There is a domain shift problem between images captured in adverse weather and those taken in clear weather. In this paper, we propose a robust detection framework called TTSDA-YOLO (Two Training Stage Domain Adaptation YOLO), which performs well in both normal and adverse weather conditions based on YOLOv7. We design a new training strategy that fully utilizes auxiliary domains to transfer knowledge from the source domain to the target domain. This training strategy consists of two stages. In the First Training Stage, we address the disparity in feature distributions between normal weather images and adverse weather images. We use a multi-scale image-level domain adaptation module to gradually adapt the normal weather domain to the adverse weather domain. In the Second Training Stage, we make full use of the auxiliary domain by inputting it into the network as a training set. To prevent new domain shifts from being generated during the training process, we design a Backbone Regularization Module (BRM). Extensive experimental results of the proposed TTSDA-YOLO on benchmark datasets show that our approach can significantly improve the detection performance of the network in adverse weather conditions.

Image-to-image steganography refers to the practice of hiding secret image within a cover image, serving as a crucial technique for secure communication and data protection. Existing image-to-image generative adversarial network-based steganographic methods for image hiding demonstrate a high embedding capacity. However, there is still significant room for improvement in terms of the quality of the stego images and the extracted secret images. In this study, we propose an architecture for inconspicuously hiding an image within the Y channel of another image, leveraging a U-Net network and a multi-scale fusion ExtractionBlock. The network is jointly trained using a loss function combining Perceptual Path Length (PPL) and Mean Square Error (MSE). The proposed network is trained and tested on two datasets, Labeled Faces in the Wild and Pascal visual object classes. Experimental results demonstrate that the model not only achieves high invisibility and significant hiding capacity (8 bits per pixel) without altering the color information of the cover image but also exhibits strong generalization ability. Additionally, we introduce the Modified Multi-Image Similarity Metric (MMISM), which integrates the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM) values of images, to comprehensively evaluate the network’s hiding and extraction capabilities.

Automotive radar is one of the key sensors for intelligent driving. Radar image sequences contain abundant spatial and temporal information, enabling target classification. For existing radar spatiotemporal classifiers, multi-view radar images are usually employed to enhance the information of the target and 3D convolution is employed for spatiotemporal feature extraction. These models consume significant hardware resources and are not applicable to real-time applications. In this paper, RadarTCN, a novel lightweight network, is proposed that achieves high-accuracy online target classification using single-view radar image sequences only. In RadarTCN, 2D convolution and 3D-TCN are employed to extract spatiotemporal features sequentially. To reduce data dimensionality and computational complexity, a multi-layer max pooling down-sampling method is designed in a 2D convolution module. Meanwhile, the 3D-TCN module is improved through residual pruning and causal convolution is introduced for leveraging the performance of online target classification. The experimental results demonstrate that RadarTCN can achieve high-precision online target recognition for both range-angle and range-Doppler map sequences. Compared to the reference models on the CARRADA dataset, RadarTCN exhibits better classification performance, with fewer parameters and lower computational complexity.

In recent years, with the emergence of convolutional neural networks, object detection technology has made rapid progress. However, object detection in remote sensing images remains a significant challenge due to the characteristics of remote sensing images, such as complex geographical environments, insufficient feature representation for small objects and large variation in object scale. These characteristics can lead to a significant decline in detector performance. In this paper, to address these issues, we propose an efficient detector called the Scale Sensitivity Mamba Network (SSMNet). SSMNet includes an IoU-based bounding box loss function: Area-IoU (AIoU), along with two novel Mamba-based modules: the Remote Sensing Mamba Block (RSMB) and the Sparse Feature Fusion Module (SFFM). The AIoU is used to make the detector more sensitive to the scale of the objects and to accelerate convergence on small object datasets. The RSMB leverages the powerful modeling capabilities of Mamba while maintaining the advantage of linear computational complexity to perform global context modeling on shallow features of remote sensing images. Meanwhile, the SFFM fuses the shallow features from the RSMB with the deep semantic features at the detection head to enhance the feature representation of small objects. Extensive experiments on three challenging remote sensing dataset, USOD, DIOR and DOTA, results reached 91.1% mAP50, 62.0% mAP50:95 and 24.6% mAP50:95 respectively, with only 30.4 GFLOPs, which demonstrates the effectiveness of our method. The code will be available at: https://github.com/BeStrongCode/SSMNet.