Achieving Real-Time Object Detection on MobileDevices with Neural Pruning Search

• URL: http://arxiv.org/abs/2106.14943v1
• Date: Mon, 28 Jun 2021 18:59:20 GMT
• Title: Achieving Real-Time Object Detection on MobileDevices with Neural Pruning Search
• Authors: Pu Zhao, Wei Niu, Geng Yuan, Yuxuan Cai, Bin Ren, Yanzhi Wang, Xue Lin
• Abstract summary: We propose a compiler-aware neural pruning search framework to achieve high-speed inference on autonomous vehicles for 2D and 3D object detection. For the first time, the proposed method achieves computation (close-to) real-time, 55ms and 99ms inference times for YOLOv4 based 2D object detection and PointPillars based 3D detection.
• Score: 45.20331644857981
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Object detection plays an important role in self-driving cars for security development. However, mobile systems on self-driving cars with limited computation resources lead to difficulties for object detection. To facilitate this, we propose a compiler-aware neural pruning search framework to achieve high-speed inference on autonomous vehicles for 2D and 3D object detection. The framework automatically searches the pruning scheme and rate for each layer to find a best-suited pruning for optimizing detection accuracy and speed performance under compiler optimization. Our experiments demonstrate that for the first time, the proposed method achieves (close-to) real-time, 55ms and 99ms inference times for YOLOv4 based 2D object detection and PointPillars based 3D detection, respectively, on an off-the-shelf mobile phone with minor (or no) accuracy loss.

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