The intelligent feeding strategy is the key to efficiently using bait and significantly reducing breeding costs in industrial recirculating aquaculture.Therefore,this study proposes an intelligent feeding strategy that combines texture discrimination of water surface images and residual baits detection based on YOLOv5-BCH.Firstly,taking the calm water surface as the baseline,the residual baits recognition frame is obtained through the texture features of the water surface image during the feeding process.The BottleNet-CSP module is employed to enhance the Backbone,and the CBAM module is utilized in the Neck,improving spatial and channel dimensions.This integration effectively fuses multi-scale features.Furthermore,to enhance the detection accuracy further,we introduce three micro-scale detection heads in the Head section.This configuration enhances the network's capacity to detect small targets on the water surface,resulting in significant improvements in mAP0.5(40.26％),mAP0.5∶0.95(15.59％),and Precision(37.85％).The adaptive feeding strategy of"trial feeding+single wheel multiple times"is adopted,effectively reducing labor input and feed waste.The results show that this system can replace manual feeding and achieve intelligent feeding throughout the process,providing a valuable reference for realizing unmanned feeding in industrial aquaculture.
维普
万方数据