簡介

Object detection in the computer vision area has been extensively studied and making tremendous progress in recent years using deep learning methods. However, due to the heavy computation required in most deep learning-based algorithms, it is hard to run these models on embedded systems, which have limited computing capabilities. In addition, the existing open datasets for object detection applied in ADAS applications usually include pedestrian, vehicles, cyclists, and motorcycle riders in western countries, which is not quite similar to the crowded Asian countries like Taiwan with lots of motorcycle riders speeding on city roads, such that the object detection models training by using the existing open datasets cannot be applied in detecting moving objects in Asian countries like Taiwan.

In this competition, we encourage the participants to design object detection models that can be applied in Taiwan’s traffic with lots of fast speeding motorcycles running on city roads along with vehicles and pedestrians. The developed models not only fit for embedded systems but also achieve high accuracy at the same time.

This competition is divided into two stages: qualification and final competition.

• Qualification competition: all participants submit their answers online. A score is calculated. The top 15 teams would be qualified to enter the final round of the competition.
  

• Final competition: the final score will be evaluated on MediaTek Dimensity 1000 Series platform for the final score.

The goal is to design a lightweight deep learning model suitable for constrained embedded system design to deal with traffic in Asian countries like Taiwan. We focus on detection accuracy, model size, computational complexity, performance optimization and the deployment on MediaTek’s Dimensity 1000 platform.

With MediaTek’s Dimensity 1000 platform and its heterogeneous computing capabilities such as CPUs, GPUs and APUs (AI processing units) embedded into the system-on-chip products, developers are provided the high performance and power efficiency for building the AI features and applications. Developers can target these specific processing units within the system-on-chip or, they can also let MediaTek NeuroPoint SDK intelligently handle the processing allocation for them.

Given the test image dataset, participants are asked to detect objects belonging to the following four classes {pedestrian, vehicle, scooter, bicycle} in each image, including class, bounding box, and confidence.

獎項

According to the points of each team in the final evaluation, we select the highest three teams for regular awards.

1. Champion: $USD 1,500

2. 1^st Runner-up: $USD 1,000

3. 2^nd Runner-up: $USD 750

Special Awards

1. Best accuracy award – award for the highest mAP in the final competition: $USD 200;

2. Best bicycle detection award – award for the highest AP of bicycle recognition in the final competition: $USD 200;

3. Best scooter detection award – award for the highest AP of scooter recognition in the final competition: $USD 200;

All the award winners must agree to submit contest paper and attend the ACM ICMR2021 Grand Challenge PAIR Competition Special Session to present their work.