Summary of Mirror Target Yolo: An Improved Yolov8 Method with Indirect Vision For Heritage Buildings Fire Detection, by Jian Liang and Junsheng Cheng
Mirror Target YOLO: An Improved YOLOv8 Method with Indirect Vision for Heritage Buildings Fire Detection
by Jian Liang, JunSheng Cheng
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed Mirror Target YOLO (MITA-YOLO) method integrates indirect vision deployment and an enhanced detection module to address the challenges of traditional fire detection systems in heritage buildings. MITA-YOLO uses mirror angles to achieve indirect views, solving issues with limited visibility in irregular spaces and aligning each indirect view with the target monitoring area. The Target-Mask module is designed to automatically identify and isolate the indirect vision areas in each image, filtering out non-target areas. This enables the model to inherit managers’ expertise in assessing fire-risk zones, improving focus and resistance to interference. The method is evaluated on an 800-image fire dataset with indirect vision, showing significant reduction in camera requirements while achieving superior detection performance compared to other mainstream models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Fires can damage heritage buildings, so it’s crucial to detect them quickly. Traditional methods use cameras, but this can harm the buildings and trigger false alarms. A new method called MITA-YOLO solves these problems by using indirect vision. This means that instead of looking directly at a fire, the system looks at a mirror reflection of the area. The MITA-YOLO model is designed to automatically identify and isolate areas that need attention, allowing it to work with limited visibility and reduce false alarms. |
Keywords
» Artificial intelligence » Attention » Mask » Yolo
