Summary of the 2021 Embedded Deep Learning Object Detection Model Compression Competition for Traffic in Asian Countries

Abstract

The 2021 embedded deep learning object detection model compression competition for traffic in Asian countries held in IEEE ICMR2021 Grand Challenges focuses on the object detection technologies in autonomous driving scenarios. The competition aims to detect objects in traffic with low complexity and small model size in the Asia countries (e.g., Taiwan), which contains several harsh driving environments. The target detected objects include vehicles, pedestrians, bicycles and crowded scooters. There are 89,002 annotated images provided for model training and 1,000 images for validation. Additional 5,400 testing images are used in the contest evaluation process, in which 2,700 of them are used in the qualification stage competition, and the rest are used in the final stage competition. There are in total 308 registered teams joining this competition this year, and the top 15 teams with the highest detection accuracy entering the final stage competition, from which 9 teams submitted the final results. The overall best model belongs to team followed by team Deep Learner and team UCBH. Two special awards of best accuracy award best and bicycle detections go to the same team as798792, and the other special award of scooter detection goes to team abcda.