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Volume 14
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10.3390/foods14101772
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Advancing Real-Time Food Inspection: An Improved YOLOv10-Based Lightweight Algorithm for Detecting Tilapia Fillet Residues

by
Zihao Su
1,2,
Shuqi Tang
1,2 and
Nan Zhong
1,2,*
1
College of Engineering, South China Agricultural University, Guangzhou 510642, China
2
Guangdong Provincial Key Laboratory of Agricultural Artificial Intelligence, Guangzhou 510642, China
*
Author to whom correspondence should be addressed.
Foods 2025, 14(10), 1772; https://doi.org/10.3390/foods14101772
Submission received: 25 March 2025 / Revised: 29 April 2025 / Accepted: 15 May 2025 / Published: 16 May 2025
(This article belongs to the Section Food Analytical Methods)
Versions Notes

Abstract

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 mAP (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.
Keywords: lightweight model; YOLOv10; object detection; tilapia processing lightweight model; YOLOv10; object detection; tilapia processing

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MDPI and ACS Style

Su, Z.; Tang, S.; Zhong, N. Advancing Real-Time Food Inspection: An Improved YOLOv10-Based Lightweight Algorithm for Detecting Tilapia Fillet Residues. Foods 2025, 14, 1772. https://doi.org/10.3390/foods14101772

AMA Style

Su Z, Tang S, Zhong N. Advancing Real-Time Food Inspection: An Improved YOLOv10-Based Lightweight Algorithm for Detecting Tilapia Fillet Residues. Foods. 2025; 14(10):1772. https://doi.org/10.3390/foods14101772

Chicago/Turabian Style

Su, Zihao, Shuqi Tang, and Nan Zhong. 2025. "Advancing Real-Time Food Inspection: An Improved YOLOv10-Based Lightweight Algorithm for Detecting Tilapia Fillet Residues" Foods 14, no. 10: 1772. https://doi.org/10.3390/foods14101772

APA Style

Su, Z., Tang, S., & Zhong, N. (2025). Advancing Real-Time Food Inspection: An Improved YOLOv10-Based Lightweight Algorithm for Detecting Tilapia Fillet Residues. Foods, 14(10), 1772. https://doi.org/10.3390/foods14101772

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