Application of YOLOv5m Network Based on Deep Learning in Aquaculture Detection

This article proposes an improved pig eye object detection method based on YOLOv5m, which adopts a multi head self attention mechanism on top of the original object detection attention mechanism. The multi head self attention mechanism is added to feature fusion and a new network model is established. The residual module in YOLOv5m is compared with the existing YOLOv5m through ablation experiments, and the improved model has a mAP of 0.969, with accuracy The effective recall rate is superior to other feature recognition models. This method improves the detection ability of the network model for small targets, effectively reducing the missed detection rate and false detection rate.