Video object detection algorithm based on deformable convolution and attention mechanism
The blurring,occlusion,and deformation of targets in video frames are important factors affecting the accuracy of video object detection.To address these issues,a DG-YOLOv8n algorithm is proposed.Firstly,the C2f module in the backbone net-work was redesigned based on deformable convolution to enhance its ability to handle target changes.Secondly,the GAM global at-tention mechanism is introduced into the neck network to amplify the global interactive representation and improve the performance of the algorithm.Finally,the experimental results on the ImageNet VID dataset showed that the improved DG-YOLOv8n algorithm had an average precision of 84.5%,which was 6.1 percentage point higher than the original YOLOv8n algorithm,verifying the effec-tiveness of the improved algorithm.
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