Research on automatic classification and detection of chicken parts based on deep learning algorithm

作者：Yan Chen, X. -S. Peng, Lu Cai, Jiao Ming, Dandan Fu, Chen Xu, Peng Zhang · 发表于：Journal of Food Science · 年份：2023 · DOI：10.1111/1750-3841.16747 · 被引用次数：4 · 研究领域：Identification and Quantification in Food、Food Supply Chain Traceability、Industrial Vision Systems and Defect Detection

Accurate classification and identification of chicken parts are critical to improve the productivity and processing speed in poultry processing plants. However, the overlapping of chicken parts has an impact on the effectiveness of the identification process. To solve this issue, this study proposed a real-time classification and detection method for chicken parts, utilizing YOLOV4 deep learning. The method can identify segmented chicken parts on the assembly line in real time and accurately, thus improving the efficiency of poultry processing. First, 600 images containing multiple chicken part samples were collected to build a chicken part dataset after using the image broadening technique, and then the dataset was divided according to the 6:2:2 division principle, with 1200 images as the training set, 400 images as the test set, and 400 images as the validation set. Second, we utilized the single-stage target detector YOLO to predict and calculate the chicken part images, obtaining the categories and positions of the chicken leg, chicken wing, and chicken breast in the image. This allowed us to achieve real-time classification and detection of chicken parts. This approach enabled real-time and efficient classification and detection of chicken parts. Finally, the mean average precision (mAP) and the processing time per image were utilized as key metrics to evaluate the effectiveness of the model. In addition, four other target detection algorithms were introduced for compari...