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

Abstract

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.

Keywords

• Real time polyp detection
• Yolov5
• Yolov6
• Object detection
• Computer aided diagnosis

Gerçek Zamanlı Polip Tespiti: YOLOv5 ve YOLOv6'nın Hız ve Performans Analizi

Abstract

Kolorektal kanser, kolonoskopi sırasında gözden kaçan poliplerin bilgisayar destekli teşhis sistemi ile tespit edilmesiyle potansiyel olarak önlenebilir. Bu nedenle, endoskopi uzmanlarına yardımcı olmak amacıyla, polipleri gerçek zamanlı olarak tespit eden bir teşhis algoritması geliştirildi. Polip tespiti için you look only once v5 (yolov5) ve you look only once v6 (yolov6) modelleri kullanıldı. Açık kaynaklı verilere ek olarak, nesne tespiti modellerini eğitmek için yeni bir özel veri seti de kullanıldı. Sonuçlara göre, yolov5x ve yolov6l sırasıyla 0.896 ve 0.913 mean average precision 50 (mAP50) oranlarına ulaştı. Yolov5x ve yolov6l karşılaştırıldığında, yolov5x'in hassasiyet açısından daha iyi olduğu, yolov6l'nin ise duyarlılık açısından daha iyi olduğu sonucuna varıldı. Modeller diğer çalışmalardaki sonuçlarla karşılaştırıldığında, yolov5x 0.876 f1-skoru oranıyla diğer çalışmalardan daha iyi performans sergilerken, yolov6l 0.893 duyarlılık oranıyla diğer çalışmaları geride bıraktı.

Keywords

• Gerçek zamanlı polip tespiti
• Yolov5
• Yolov6
• Bilgisayar destekli tanı
• Nesne tespiti

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