Polyp-YOLOv5-Tiny: A Lightweight Model for Real-Time Polyp Detection
Shimin Ou, Yixing Gao, Zebin Zhang, Chenjian Shi · 2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) · 2021
Early colonoscopy diagnosis can significantly reduce the mortality rate of colon cancer patients. Deep learning based object detection assists to enhance clinical performance on accurate diagnosis; however, it is challenging to integrate current object detection models on low-performance endoscope hardware devices. This paper presents a lightweight model for real-time polyp detection. By reducing the number of convolutional kernels by half and removing the large-object-detecting head from YOLOv5, our model got a similar precision to YOLOv3-spp (mAP.5:.95 of 0.591 and 0.583 respectively), but with a model size of only 2.8 MB (119.7 MB of YOLOv3-spp). There is a slight loss in precision compared to YOLOv5s (mAP.5:.95 of 0.636 with 13.7MB model size); however, our model still shows a significant advantage on reducing model complexity. Experimental results also indicated that MS COCO pre-training is helpful in polyp detection tasks, and the Mosaic augmentation strongly enhances the precision of YOLO models especially when the training set is small.