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In this paper, the limitations of YOLOv5s model on small target detectiontask are deeply studied and improved. The performance of the model issuccessfully enhanced by introducing GhostNet-based convolutional module,RepGFPN-based Neck module optimization, CA and Transformer's attentionmechanism, and loss function improvement using NWD. The experimental resultsvalidate the positive impact of these improvement strategies on modelprecision, recall and mAP. In particular, the improved model shows significantsuperiority in dealing with complex backgrounds and tiny targets in real-worldapplication tests. This study provides an effective optimization strategy forthe YOLOv5s model on small target detection, and lays a solid foundation forfuture related research and applications.

Improvement and Enhancement of YOLOv5 Small Target Recognition Based on Multi-module Optimization