Construction of a Food Packaging Safety Closure Detection System Based on Improved Yolo Algorithm

Traditional manual inspection methods are prone to problems such as leakage and untimely detection in food packaging inspection, so there is an urgent need to adopt intelligent computer vision technology to realize efficient detection of food packaging defects. To this end, this paper proposes a food packaging safety seal detection system based on the improved YOLO algorithm. First, the network structure and training strategy are optimized for the food packaging detection task, including the adoption of a backbone network that is more suitable for small object detection and the design of a more reasonable anchor frame. Then, the closure detection system constructed by CNN algorithm is selected as a comparison model, and the performance of the package closure detection system constructed by the two algorithmic models is compared and analyzed in terms of recall and F1 value under the same dataset and iteration conditions. The experimental results show that the detection system constructed by the improved YOLO algorithm is significantly better than the detection system constructed by the CNN algorithm in the food packaging defect detection task. With the same number of iterations, the recall and F1 value of YOLO are always higher than that of CNN, and the gap is getting bigger and bigger as the number of iterations increases. In summary, the improved YOLO algorithm significantly improves the detection efficiency and accuracy of food packaging safety seals and provides an effective solution for practical applications.