Abstract
[Objective] To achieve rapid and non -destructive online detection of the appearance quality of crayfish during primary processing and to improve detection accuracy and real -time performance in complex scenarios. [Methods] A dataset for crayfish appearance quality is constructed for complex scenarios on primary -processing production lines, and a crayfish appearance quality detection algorithm YOLO -CQ based on YOLOv 11n is proposed. DualConv is introduced to improve the C 3k2 module, enhancing the model's feature extraction capability while optimizing the allocation of computational resources. Deformable attention (DAT ) is integrated into the C 2PSA module, enabling the model to adaptively focus on key regions. In addition, an IoU -based dynamically weighted SlideLoss is adopted to optimize the classification loss, thereby balancing the gradient contributions of samples with different difficulty levels during training. [Results] YOLO -CQ achieves the precision of 86.6%, the recall of 84.3%, and the mAP@ 0.5 of 91.3%, which represent improvements of 2.5%, 2.3%, and 3.4% over those of YOLOv 11n, respectively. YOLO -CQ reaches an inference speed of 211.5 FPS. [Conclusion] YOLO -CQ significantly improves the detection accuracy of crayfish appearance quality while maintaining model lightness and real -time performance, meeting the requirements of online grading on primary -processing production lines.
Publication Date
10-21-2026
First Page
83
Last Page
93
DOI
10.13652/j.spjx.1003.5788.2025.81065
Recommended Citation
Xujia, Wang; Shuqing, Wang; and Haoyu, Hu
(2026)
"A non-destructive appearance quality detection method for primary-processed crayfish based on improved YOLOv 11,"
Food and Machinery: Vol. 42:
Iss.
9, Article 10.
DOI: 10.13652/j.spjx.1003.5788.2025.81065
Available at:
https://www.ifoodmm.cn/journal/vol42/iss9/10
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