Detection and Tracking of Broiler Flock Movements in The Chicken Coop using YOLO
Wiwit Agus Triyanto, Kusworo Adi, Jatmiko Endro Suseno · E3S Web of Conferences · 2023
Observation of the movement of broilers in the chicken coop is done to monitor the welfare and health condition of broilers. Currently, observing broiler flock activity in the chicken coop is generally still done conventionally, with manual observations made by farmers. But on a large scale, this observation method takes a lot of time and manpower, and is subjective. Therefore, an automatic observation system is needed that continuously monitors broiler activity, so as to increase the efficiency of farmer resources and reduce operational costs for observing broiler activity. This study developed an automatic detection and tracking system for broiler chicken movements using You Only Look Once, Version 4 (YOLOv4) as the base model, Yolo Weights as the Transfer Learning Pretrained Model, and Deep Sort as the Tracker Model. For comparison of base models, use Single Shot Multibox Detector (SSD), You Only Look Once, Version 3 (YOLOv3), You Only Look Once, Version 4 - tiny (YOLOv4-tiny). For comparison, the Network Model uses MobileNet and MobileNet v2. For comparison of Transfer Learning Pretrained Model using Caffe Model Weights and Tensorflow Weights. For comparison of Tracker Models using Centroid Tracker, Centroid Kalman Filter Tracker, Simple Online and Realtime Tracking (SORT) and Intersection over Union (IOU). The results showed that Model You Only Look Once, Version 4 (YOLOv4) with Transfer Learning Pretrained Model = Yolo Weights, and Tracker Model = Deep SORT was able to detect and track the most chicken herds in cages compared to others, with the number of broilers detected as many as 17.