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@article{Huang_Tian_Tian_2023, title={Research on Semantic Segmentation of Fish-Eye Images for Autonomous Driving}, volume={10}, url={https://www.zealpress.com/index.php/ijrat/article/view/544}, DOI={10.31875/2409-9694.2023.10.13}, abstractNote={<p><strong>Abstract:</strong> Fisheye cameras, valued for their wide field of view, play a crucial role in perceiving the surrounding environment of vehicles. However, there is a lack of specific research addressing the processing of significant distortion features in segmenting fish-eye images. Additionally, fish-eye images for autonomous driving face the challenge of few datasets, potentially causing over fitting and hindering the model’s generalization ability.</p> <p>Based on the semantic segmentation task, a method for transforming normal images into fish-eye images is proposed, which expands the fish-eye image dataset. By employing the Transformer network and the Across Feature Map Attention, the segmentation performance is further improved, achieving a 55.6% mIOU on Woodscape. Additionally, leveraging the concept of knowledge distillation, the network ensures a strong generalization based on dual-domain learning without compromising performance on Woodscape (54% mIOU).</p>}, journal={International Journal of Robotics and Automation Technology}, author={Huang, Hongtao and Tian, Xiaofeng and Tian, Wei}, year={2023}, month={Dec.}, pages={138–148} }