Abstract
Abstract In order to address issues that arise in complex scenarios, an edge-enhanced YOLOv5 algorithm is proposed. The fusion of edge features with original feature maps serves to enhance the accuracy of the detection process. The algorithm employs Sobel and Canny operators for edge extraction, integrating these with low-level feature maps through addition and concatenation. Testing in urban and desert settings demonstrated that the enhanced models exhibited superior accuracy and a reduction in false and missed detection compared to the original YOLOv5. This methodology has the potential to enhance target detection performance in complex environments.
Published Version
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