Su Zihao, Tang Shuqi, Zhong Nan
College of Engineering, South China Agricultural University, Guangzhou 510642, China.
Guangdong Provincial Key Laboratory of Agricultural Artificial Intelligence, Guangzhou 510642, China.
Foods. 2025 May 16;14(10):1772. doi: 10.3390/foods14101772.
Tilapia fillet is an aquatic product of great economic value. Detection of impurities on tilapia fillet surfaces is typically performed manually or with specialized optical equipment. These residues negatively impact both the processing quality and the economic value of the product. To solve this problem, this study proposes a tilapia fillet residues detection model, the double-headed GC-YOLOv10n; the model is further lightweighted and achieves improved detection performance compared to the double-headed GC-YOLOv10n. The model demonstrates the best overall performance among many mainstream detection algorithms with a small model size (3.3 MB), a high frame rate (77FPS), and an excellent (0.942). It is able to complete the task of tilapia fillet residues detection with low cost, high efficiency, and high accuracy, thus effectively improving the product quality and production efficiency of tilapia fillets.
罗非鱼片是一种具有很高经济价值的水产品。罗非鱼片表面杂质的检测通常是人工进行或使用专门的光学设备。这些残留物会对产品的加工质量和经济价值产生负面影响。为了解决这个问题，本研究提出了一种罗非鱼片残留物检测模型——双头GC-YOLOv10n；该模型进一步轻量化，与双头GC-YOLOv10n相比，检测性能有所提高。该模型在众多主流检测算法中表现出最佳的整体性能，模型尺寸小（3.3MB）、帧率高（77FPS）且准确率优异（0.942）。它能够以低成本、高效率和高精度完成罗非鱼片残留物检测任务，从而有效提高罗非鱼片的产品质量和生产效率。
