Abstract
Evaluating car damages from misfortune is critical to the car insurance industry. However, the accuracy is still insufficient for real-world applications since the deep learning network is not designed for car damage images as inputs, and its segmented masks are still very coarse. This paper presents MARS (Mask Attention Refinement with Sequential quadtree nodes) for car damage instance segmentation. Our MARS represents self-attention mechanisms to draw global dependencies between the sequential quadtree nodes layer and quadtree transformer to recalibrate channel weights and predict highly accurate instance masks. Our extensive experiments demonstrate that MARS outperforms state-of-the-art (SOTA) instance segmentation methods on three popular benchmarks such as Mask R-CNN [9], PointRend [13], and Mask Transfiner [12], by a large margin of +1.3 maskAP-based R50-FPN backbone and +2.3 maskAP-based R101-FPN backbone on Thai car-damage dataset. Our demos are available at https://github.com/kaopanboonyuen/MARS.
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Acknowledgement
This work was partially supported by Thaivivat Insurance PCL, by providing financial support and expertise in car insurance. We thank Natthakan Phromchino, Darakorn Tisilanon, and Chollathip Thiangdee in MARS (Motor AI Recognition Solution) for annotating the data. Teerapong Panboonyuen and his colleague received no financial support for this article’s research, authorship, and/or publication.
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Panboonyuen, T., Nithisopa, N., Pienroj, P., Jirachuphun, L., Watthanasirikrit, C., Pornwiriyakul, N. (2024). MARS: Mask Attention Refinement with Sequential Quadtree Nodes for Car Damage Instance Segmentation. In: Foresti, G.L., Fusiello, A., Hancock, E. (eds) Image Analysis and Processing - ICIAP 2023 Workshops. ICIAP 2023. Lecture Notes in Computer Science, vol 14365. Springer, Cham. https://doi.org/10.1007/978-3-031-51023-6_3
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