Summary of Bg-yolo: a Bidirectional-guided Method For Underwater Object Detection, by Jian Zhang et al.
BG-YOLO: A Bidirectional-Guided Method for Underwater Object Detection
by Jian Zhang, Ruiteng Zhang, Xinyue Yan, Xiting Zhuang, Ruicheng Cao
First submitted to arxiv on: 13 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel bidirectional-guided approach for underwater object detection, referred to as BG-YOLO, which addresses the issue of degraded underwater images affecting the accuracy of object detection. The method consists of two parallel branches: an enhancement branch and a detection branch. The enhancement branch includes image enhancement and object detection subnets, while the detection branch only has a detection subnet. A feature guided module connects the shallow convolution layers of both branches, guiding the optimization process. This approach refines the detection performance in severely degraded underwater scenes while maintaining a remarkable detection speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For those who want to detect objects underwater, but struggle with poor image quality, this paper offers a solution! The authors designed a special way to enhance images and improve object detection at the same time. They created two parts: one that makes the image better and another that finds the object. By connecting these parts together, they made sure both work well together. This method works really fast and can even detect objects in very poor underwater conditions. |
Keywords
* Artificial intelligence * Object detection * Optimization * Yolo
