Summary of the 2023 PAIR-LITEON Competition: Embedded AI Object Detection Model Design Contest on Fish-eye Around-view Cameras

Yu-Shu Ni, Chia-Chi Tsai, Jyun-Syu Lin, Hsien-Po Meng, Po-Chi Hu, Jiun-Shiung Chen, Kun-Hung Lin, Chih-Yuan Chuang, Jiun-In Guo

Abstract: This competition is dedicated to achieving fisheye object detection in Asia, particularly in countries like Taiwan, while emphasizing low power consumption and simultaneously achieving a high mean average precision (mAP). This task is notably challenging as it must be accomplished in adverse driving conditions. The objects targeted for detection include cars, pedestrians, motorcycles, and bicycles. To train their models, participants utilized 89,002 annotated training images from the iVS-Dataset [1] and conducted testing on the MemryX platform [2]. To excel in this competition, participants had to master the art of transforming standard images into fisheye images. The judging process involved 6,500 test images, with 1,500 used in the preliminary competition stage, and the rest reserved for the final competition stage. A total of 129 teams registered for this competition, and those with mAP scores exceeding 20% advanced to the final competition stage, where 16 teams are qualified. Out of these, 11 teams submitted their works based on the final competition accuracy, which could not be lower than 5% of the preliminary competition accuracy. Ultimately, five teams attained their final scores and competed for rankings based on paper reviews. Champion is chici_lab, securing the top position in this demanding competition. NCKU_ACVLab, the 1st Runner-up, demonstrated outstanding skills. The 2nd Runner-up, yuhsi44165, also showcased commendable performance. Special Awards recognized excellence in specific categories, with chici_lab sweeping all three accolades. They were bestowed the best pedestrian detection award, the best bicycle detection award, and the best motorbike detection award for their remarkable achievements.