A Lightweight Multi-Scale Object Detection Framework for Shrimp Meat Quality Control in Food Processing

Reliable quality and size inspection of shrimp meat is essential in food processing to ensure food safety, enhance production efficiency, and promote sustainable practices. However, significant scale differences in shrimp meat categories and the presence of subtle local defects pose challenges to traditional manual inspection methods, resulting in low efficiency and high rates of false positives and negatives. To address these challenges, we propose a lightweight multi-scale object detection framework specifically designed for automated shrimp meat inspection in food processing environments. Our framework incorporated a novel downsampling module (ADown) that was engineered to reduce parameters while preserving essential features. Additionally, we propose dual-scale information selection convolution (DSISConv), multi-scale information selection convolution (MSISConv), and a lightweight multi-scale information selection detection head (LMSISD) to improve detection accuracy across diverse object scales. Furthermore, a bidirectional complementary knowledge distillation strategy was employed, which enabled the lightweight model to learn crucial features from a larger teacher model without increasing inference complexity. Experimental results validated the effectiveness of our approach. Compared to the YOLOv11n (baseline) model, the proposed framework improved precision by 1.0%, recall by 0.8%, mAP50 by 0.9%, and mAP50-95 by 1.3%, while simultaneously reducing parameters by 7.1%, model size by 8.0%, and GFLOPs by 22.2%. The application of knowledge distillation yielded further improvements of 0.1% in precision, 1.2% in recall, 0.5% in mAP50, and 0.5% in mAP50-95. These results indicated that the proposed approach provided an effective and efficient solution for real-time shrimp meat inspection, balancing high accuracy with low computational requirements.