Speed-Up of Object Detection Neural Network with GPU

We realized a speed-up of an object detection neural network with GPU. We improved the object detection speed of faster R-CNN [1], which is one of the most commonly used detection networks [2]. The speed of the original faster R-CNN (py - faster - rcnn [3]) was 72.4ms per image on our GPU server 11OS: Ubuntu 14.04.5 LTS (GNU/Linux 4.2.0-42-generic x86_64), CPU: Intel(R) Xeon(R) CPU E5-2680 v4 @ 2.40GHz, GPU: GPU (Tesla P100-PCIE-16GB) x 1, Libraries: MKL, CUDA 8.0, cuDNN v5.1.5. We accelerated the detection speed by implementing our new algorithms that are suitable for GPUs. The speed-up is realized without sacrificing the object detection accuracy (mAP). Our GPU-accelerated faster R-CNN can detect objects with 55.8ms per image. This is nearly 30% speed-up. In detection networks, the processes of building scored candidate regions, sorting and non-maximum-suppression (nms) are commonly used. In faster R-CNN, these processes are executed in proposal layer. We reduced the processing time of the proposal layer from 5.6ms to 2.2ms. This is 2.5 times as fast as the original one. We also evaluated the detection speed with larger batch sizes. By applying batch size 16, it is accelerated to 44.9ms per image. This is 1.6 times as fast as the original faster R-CNN (py-faster-rcnn). Since we realized a speed-up of common basic methods for detection networks, our speed-up methods are also applicable to other detection networks such as R-FCN [4], YOLO [5] [6] and SSD [7].

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