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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 "as798792", 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."

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To overcome the limitations of standard datasets with data at a wide-variety of scales and captured in the various conditions necessary to train neural networks to yield efficient results in ADAS applications, this paper presents a self-built open-to-free-use ‘iVS dataset’ and a data annotation tool entitled ‘ezLabel’. The iVS dataset is comprised of various objects at different scales as seen in and around real driving environments. The data in the iVS dataset are collected by employing a camcorder in vehicles driving under different conditions, e.g., light, weather and traffic, and driving scenarios ranging from city traffic during peak and normal hours to freeway traffics during busy and normal conditions. Thus, the collected data are wide-ranging and captured all possible objects at various scales appearing in real-time driving situations. The data collected in order to build the dataset has to be annotated before use in training the CNNs and so this paper presents an open-to-free-use data annotation tool, ezLabel, for data annotation purposes as well.