In aquaculture, the accumulation of leftover feed on the water surface not only leads to wastage but also contributes to deteriorating water quality, significantly impacting the well-being and growth of aquatic organisms. Conventional detection methods face challenges in accurately identifying small feed particles due to the intricate nature of aquatic environments. To tackle this issue, this research introduces an enhanced algorithm based on YOLOv8n for detecting residual feed on water surfaces.This algorithm improves the precision of detecting small feed particles by incorporating a specialized detection layer tailored for small targets. By amalgamating shallow and deep feature information, the algorithm enhances the network's ability to perceive targets across various scales, thereby boosting the accuracy of detecting small feed particles. Furthermore, the integration of the C2f_Faster_EMA module reduces model parameters, elevates detection speed, and fortifies the extraction of intricate features. Additionally, the devised ICBAM module bolsters the amalgamation of feature information for small targets, significantly enhancing detection accuracy.Experimental findings illustrate that the enhanced algorithm delivers exceptional performance across multiple evaluation metrics. Comparative to the original YOLOv8n, the @0.5, precision, and recall rates have surged by 10.3%, 7.6%, and 10.2%, respectively. Furthermore, the algorithm achieves a detection speed of 125 frames per second FPS, meeting the demands for real-time detection. These outcomes underscore the algorithm's capacity to swiftly and accurately identify residual feed on water surfaces, providing substantial technical backing for the intelligent administration of aquaculture.The implementation of this algorithm holds promise in efficiently curbing feed wastage, enhancing water quality, and amplifying the profitability of aquaculture operations. This advancement positions the aquaculture sector on a trajectory towards a more sustainable and efficient future.
ZHENG Haifeng, JIANG Linyuan, WEN Luting, ZHOU Xiushan, JIE Baifei, WEN Jiayan,
. A Water surface residual feed detection algorithm based on improved YOLOv8n[J]. Fishery Modernization, 2025
, 52(1)
: 80
.
DOI: 10.3969/j.issn.1007-9580.2025.01.008
