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Object detection plays an important role in self-driving cars for securitydevelopment. However, mobile systems on self-driving cars with limitedcomputation resources lead to difficulties for object detection. To facilitatethis, we propose a compiler-aware neural pruning search framework to achievehigh-speed inference on autonomous vehicles for 2D and 3D object detection. Theframework automatically searches the pruning scheme and rate for each layer tofind a best-suited pruning for optimizing detection accuracy and speedperformance under compiler optimization. Our experiments demonstrate that forthe first time, the proposed method achieves (close-to) real-time, 55ms and99ms inference times for YOLOv4 based 2D object detection and PointPillarsbased 3D detection, respectively, on an off-the-shelf mobile phone with minor(or no) accuracy loss.

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