Animal Derived Food Detection Method Based on Improved YOLO Algorithm
Due to the irregular feature distribution of animal derived food images,the reliability of their detection results is difficult to guarantee.Therefore,a animal derived food detection method based on improved YOLO algorithm is proposed.By using the backbone feature extraction network Darknet-53 of YOLO V3,visible and infrared light in animal source food images are extracted separately.Combining the optimal weight parameters of their corresponding modal features,feature weighted fusion is performed to calculate the target box position loss,target confidence loss,and category loss of the fused features,and determine the final classification.The test results show that the recognition results of the design method for animal derived food images are stable,and the number of incorrect recognition remains at a low level,unaffected by the composition of the test dataset.
NETL
NSTL
万方数据
