Evaluation of Individual Detection Performance of Broiler Chickens Based on Object Detection Algorithm by Deep Learning

Abstract

This study was conducted to develop a labor-saving individual detection method for broiler (meat chicken) breeding management. After individual detection using an object-detection algorithm based on deep learning technology, we evaluated the accuracy of the object detection model. Using a surveillance camera installed on the breeding facility ceiling, a new object detection model was created and operated continuously for about 1 week with regular re-learning using recorded growth images. Consequently, a method for individual detection was configured. Results show that continuous detection for 5-week-old individuals can be achieved by re-learning for this amount of activity and for a weight gain rate of 20 % or less.