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Dahua Technology’s ARI Method Ranked #1 in the KITTI 2D Object Detection Evaluation 2012
August 2018 by Marc Jacob
As shown on The KITTI Vision Benchmark Suite official website, Dahua Technology’s ARI 2D object detection method took the 1st place in KITTI Vision Benchmark Suite’s Object Detection Evaluation 2012 on July 26th, 2018 - with an accuracy of 91.48% based on the moderate difficulty level.
Dahua’s ARI Method Ranked #1 in KITTI Object Detection Evaluation 2012
The KITTI vision benchmark suite is a systematic benchmarking platform designed to evaluate computer vision performance. Funded by the Karlsruhe Institute of Technology and the Toyota Technological Institute at Chicago, it is probably the world’s first and largest benchmarking suite for vision based autonomous driving. KITTI includes real life images collected from a variety of scenery, from urban streets in the mid-size city of Karlsruhe to rurals roads and highways. Each image contains sophisticated scenarios involving at most 15 vehicles and 30 pedestrians with varying levels of overlapping. The KITTI vision Benchmark suite comprises of real-world benchmarks for stereo, optical flow, visual odometry, object detection and tracking.
This competition has provided Dahua with an excellent opportunity to further its independent research and development of deep learning algorithms. Based upon the advantages of network structures such as ResNet, Dahua Technology has successfully improved the structures of its deep learning detection algorithm. Utilizing reinforcement learning and other training techniques, as well as multi-model fusion technology, Dahua Technology has made a significant improvement in the detection rate of small and/or overlapped targets.
