An experimental study of stunned state detection for broiler chickens using an improved convolution neural network algorithm

Abstract

Effective recognition method of broiler stunned state has always been an important issue in real industries. In recent years, recognition methods such as neural networks have been receiving increasing attention due to their great merits of high diagnostic accuracy and easy implementation. To improve the accuracy and efficiency of broiler stunned state recognition, an improved fast region-based convolutional neural network (You Only Look Once + Multilayer Residual Module (YOLO + MRM)) algorithm was proposed and applied to the recognition of three broiler stunned states: insufficient, appropriate and excessive stuns. The images were collected from a broiler-slaughtering line using a complementary metal-oxide semiconductor (CMOS) camera. The area of the head and wings of a broiler in the original image was marked according to the PASCAL VOC data format and the dataset of each broiler stunned state was obtained. The results showed that the YOLO + MRM algorithm achieved good performance with an accuracy of 96.77%. To compare YOLO + MRM with other models, similar experiments were conducted using a conventional back propagation neural network (BP-NN) classifier, as well as YOLO, and the recognition accuracies were 90.11% and 94.74%, respectively. YOLO + MRM can complete the detection task of more than 180,000 broilers per hour. Compared with the traditional method, little prior expertise on image recognition is required, the recognition accuracy and speed are improved obviously. This study has provided a foundation and highlighted the potential for automatically detecting the stunned state of broiler chickens, which is crucial for the success of an automatic electric stunning process in the poultry industry.