Real-Time Polyp Detection: A Speed and Performance Analysis of YOLOv5 and YOLOv6

Colorectal cancer can potentially be prevented by detecting polyps missed during colonoscopy using a computer aided diagnosis system. Therefore, a diagnostic algorithm, which detects polyps in real-time, was developed to assist endoscopy specialists. You look only once v5 (yolov5) and you look only once v6 (yolov6) models were used for polyp detection. In addition to open-source data, a new private dataset was also used for training object detection models. According to the results, yolov5x and yolov6l achieved mean average precision 50 (mAP50) rates of 0.896 and 0.913, respectively. When yolov5x and yolov6l were compared, it was concluded that yolov5x was better in terms of precision, while yolov6l was better in terms of recall. When models were compared with other studies in the literature, yolov5x outperformed other studies in terms of f1-score with a rate of 0.876 and yolov6l outperformed other studies in terms of recall with a rate of 0.893.