Object Detection in Visible Light and Infrared Images Based on Adaptive Attention Mechanism

To address the shortcomings of infrared and visible light object detection methods, a detection method based on an adaptive attention mechanism that combines deep learning technology with multi-source object detection is proposed. First, a dual-source feature extraction structure is constructed based on deep separable convolution to extract the features of infrared and visible objects. Second, an adaptive attention mechanism is designed to fully complement the multimodal information of the object, and the infrared and visible features are weighted and fused using a data-driven method to ensure the full fusion of features and reduce noise interference. Finally, for multiscale object detection, the adaptive attention mechanism is combined with multiscale parameters to extract and fuse the global and local features of the object to improve the scale invariance. Experiments show that the proposed method can accurately and efficiently achieve target recognition and localization in complex scenarios compared to similar object detection algorithms. Moreover, in actual substation equipment detection, this method also demonstrates higher generalization and robustness, which can effectively assist robots in completing object detection tasks.