YOLO-OB: An improved anchor-free real-time multiscale colon polyp
  detector in colonoscopy
Xiao Yang Enmin Song Guangzhi Ma Yunfeng Zhu Dongming Yu Bowen Ding Xianyuan Wang
Colon cancer is expected to become the second leading cause of cancer death
in the United States in 2023. Although colonoscopy is one of the most effective
methods for early prevention of colon cancer, up to 30% of polyps may be missed
by endoscopists, thereby increasing patients' risk of developing colon cancer.
Though deep neural networks have been proven to be an effective means of
enhancing the detection rate of polyps. However, the variation of polyp size
brings the following problems: (1) it is difficult to design an efficient and
sufficient multi-scale feature fusion structure; (2) matching polyps of
different sizes with fixed-size anchor boxes is a hard challenge. These
problems reduce the performance of polyp detection and also lower the model's
training and detection efficiency. To address these challenges, this paper
proposes a new model called YOLO-OB. Specifically, we developed a bidirectional
multiscale feature fusion structure, BiSPFPN, which could enhance the feature
fusion capability across different depths of a CNN. We employed the ObjectBox
detection head, which used a center-based anchor-free box regression strategy
that could detect polyps of different sizes on feature maps of any scale.
Experiments on the public dataset SUN and the self-collected colon polyp
dataset Union demonstrated that the proposed model significantly improved
various performance metrics of polyp detection, especially the recall rate.
Compared to the state-of-the-art results on the public dataset SUN, the
proposed method achieved a 6.73% increase on recall rate from 91.5% to 98.23%.
Furthermore, our YOLO-OB was able to achieve real-time polyp detection at a
speed of 39 frames per second using a RTX3090 graphics card. The implementation
of this paper can be found here: https://github.com/seanyan62/YOLO-OB.