Optimizing Anchor-based Detectors for Autonomous Driving Scenes

• URL: http://arxiv.org/abs/2208.06062v1
• Date: Thu, 11 Aug 2022 22:44:59 GMT
• Title: Optimizing Anchor-based Detectors for Autonomous Driving Scenes
• Authors: Xianzhi Du, Wei-Chih Hung, Tsung-Yi Lin
• Abstract summary: This paper summarizes model improvements and inference-time optimizations for the popular anchor-based detectors in autonomous driving scenes. Based on the high-performing RCNN-RS and RetinaNet-RS detection frameworks, we study a set of framework improvements to adapt the detectors to better detect small objects in crowd scenes.
• Score: 22.946814647030667
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper summarizes model improvements and inference-time optimizations for the popular anchor-based detectors in the scenes of autonomous driving. Based on the high-performing RCNN-RS and RetinaNet-RS detection frameworks designed for common detection scenes, we study a set of framework improvements to adapt the detectors to better detect small objects in crowd scenes. Then, we propose a model scaling strategy by scaling input resolution and model size to achieve a better speed-accuracy trade-off curve. We evaluate our family of models on the real-time 2D detection track of the Waymo Open Dataset (WOD). Within the 70 ms/frame latency constraint on a V100 GPU, our largest Cascade RCNN-RS model achieves 76.9% AP/L1 and 70.1% AP/L2, attaining the new state-of-the-art on WOD real-time 2D detection. Our fastest RetinaNet-RS model achieves 6.3 ms/frame while maintaining a reasonable detection precision at 50.7% AP/L1 and 42.9% AP/L2.

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