A method for detecting uneaten feed based on improved YOLOv5.

• A convolutional neural network for detecting uneaten feed is proposed. • Attention mechanism and small object detection layer are integrated into the network. • Lightweight modules are applied to improve detection speed without loss of accuracy. • The proposed algorithm can detect residual particles accurately in real-time. In order to increase feed utilization and reduce rearing costs, it is crucial to accurately detect remaining pellets in real-time and optimize feeding mechanisms for precise feeding in intensive aquaculture. Machine vision and acoustic methods, which are widely used for the detection of residual particles, fail to adequately address scenarios such as the detection of overlapping, conjoined, faraway and dense particles, as well as particles with interference from water surface ripples and fish motion. To address these issues, this paper proposed a model for uneaten feed detection based on You Only Look Once (YOLO)-v5 network, which was built as follows: (1) The last C3 module of the backbone network is introduced into the coordinated attention (CA) mechanism, which evolves into a coordinated feature attention extraction (CFAE) module to obtain global information. (2) The GSConv module, and the VoVGSCSP module which was the aggregative mode of GSConv module, were adopted in the neck network part. (3) The shallow feature map was spliced with the deep feature map, and the tiny target detection layer (DL) was added for detection. The model elucidates the true condition of the detected residual particles by enhancing detection accuracy and minimizing model complexity, enabling precise and rapid identification of the pellet. This article employs seven sets of data to conduct a comparative analysis of the outcomes achieved before and after enhancing the three major components of the network. The experimental results showed that the YOLOv5s-CAGSDL model achieved an accuracy of 94.1% for feed particles detection, effectively solving the above problems in the actual rearing environment. Meanwhile, compared with the YOLOv5s model, AP 50 and FPS of the YOLOv5s-CAGSDL model were increased by 6.30% and 10.80% respectively, bringing potential environmental and economic benefits in the actual aquaculture environment. The source codes and datasets are available at: https://github.com/AAAArmstrong/YOLOv5s-CAGSDL. [ABSTRACT FROM AUTHOR]