Object detection in remote sensing images has received significant attention for a wide range of applications. However, traditional unimodal remote sensing images, whether based on visible light or infrared, have limitations that cannot be ignored. Visible light images are susceptible to ambient lighting conditions, and their detection accuracy can be greatly reduced. Infrared images often lack rich texture information, resulting in a high false-detection rate during target identification and classification. To address these challenges, we propose a novel multimodal fusion network detection model, named ACDF-YOLO, basedon the lightweight and efficient YOLOv5 structure, which aims to amalgamate synergistic data from both visible and infrared imagery, thereby enhancing the efficiency of target identification in remote sensing imagery. Firstly, a novel efficient shuffle attention module is designed to assist in extracting the features of various modalities. Secondly, deeper multimodal information fusion is achieved by introducing a new cross-modal difference module to fuse the features that have been acquired. Finally, we combine the two modules mentioned above in an effective manner to achieve ACDF. The ACDF not only enhances the characterization ability for the fused features but also further refines the capture and reinforcement of important channel features. Experimental validation was performed using several publicly available multimodal real-world and remote sensing datasets. Compared with other advanced unimodal and multimodal methods, ACDF-YOLO separately achieved a 95.87% and 78.10% mAP0.5 on the LLVIP and VEDAI datasets, demonstrating that the deep fusion of different modal information can effectively improve the accuracy of object detection.

Insulated gate bipolar transistor (IGBT) is a power semiconductor module .Voids may arise in its solder process when a contaminant or gas is absorbed into the solder joint. They heavily influence the heat exchange efficiency of IGBT, so void inspection is very important. The segmentation of solder region is a crucial step for automated defect detection of IGBT based on x-ray computed laminography (CL) system. In recent years, deep learning has made remarkable process in semantic segmentation and has been used for the segmentation of solder joint between the direct bonded copper (DBC) substrate and baseplate, which has been proved to be accurate and efficient. However, deep learning architectures exhibit a critical drop of performance due to catastrophic forgetting when new IGBT samples encountered. Hence, this paper proposes to use online learning techniques to continuously improve the learned model by feeding new IGBT samples without losing previously learned knowledge.