Lightweight Multisource Object Detection Based on Group Feature Extraction

To balance the accuracy and efficiency of multisource object detection networks, a lightweight infrared and visible light object detection model with a multiscale attention structure and an improved object-box filtering strategy was designed by applying group convolution to multimodal object features. First, multiple feature dimensionality reduction strategies were adopted to sample the input image and reduce the impact of noise and redundant information. Subsequently, feature grouping was performed based on the mode of the feature channel, and deep separable convolution was used to extract infrared, visible, and fused features, to enhance the diversity and efficiency of extracted multisource feature structures. Then, an improved attention mechanism was utilized to enhance key multimodal features in various dimensions, combining them with a neighborhood multiscale fusion structure to ensure scale invariance of the network. Finally, the optimized non-maximum suppression algorithm was used to synthesize the prediction results of objects at various scales for accurate detection of each object. Experimental results based on the KAIST, FLIR, and RGBT public thermal datasets show that the proposed model effectively improves object detection performance compared with the same type of multisource object detection methods.