PVANET: Deep but Lightweight Neural Networks for Real-time Object ...

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PVANET: Deep but Lightweight Neural Networks for Real-time Object ...

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Sep 30, 2016 - accuracies for commercialization in a broad range of markets like automotive and .... training idea, we add residual connections onto inception layers as well to ... 4For 20-class object detection, R-CNN produces 21 predicted ...

arXiv:1608.08021v1 [cs.CV] 29 Aug 2016

PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection

Kye-Hyeon Kim, Yeongjae Cheon, Sanghoon Hong, Byungseok Roh, and Minje Park Intel Imaging and Camera Technology 21 Teheran-ro 52-gil, Gangnam-gu, Seoul 06212, Korea {kye-hyeon.kim, yeongjae.cheon, sanghoon.hong, peter.roh, minje.park}@intel.com

Abstract This paper presents how we can achieve the state-of-the-art accuracy in multicategory object detection task while minimizing the computational cost by adapting and combining recent technical innovations. Following the common pipeline of “CNN feature extraction + region proposal + RoI classification”, we mainly redesign the feature extraction part, since region proposal part is not computationally expensive and classification part can be efficiently compressed with common techniques like truncated SVD. Our design principle is “less channels with more layers” and adoption of some building blocks including concatenated ReLU, Inception, and HyperNet. The designed network is deep and thin and trained with the help of batch normalization, residual connections, and learning rate scheduling based on plateau detection. We obtained solid results on well-known object detection benchmarks: 81.8% mAP (mean average precision) on VOC2007 and 82.5% mAP on VOC2012 (2nd place), while taking only 750ms/image on Intel i7-6700K CPU with a single core and 46ms/image on NVIDIA Titan X GPU. Theoretically, our network requires only 12.3% of the computational cost compared to ResNet-101, the winner on VOC2012.

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Introduction

Convolutional neural networks (CNNs) have made impressive improvements in object detection for several years. Thanks to many innovative work, recent object detection systems have met acceptable accuracies for commercialization in a broad range of markets like automotive and surveillance. In terms of detection speed, however, even the best algorithms are still suffering from heavy computational cost. Although recent work on network compression and quantization shows promising result, it is important to reduce the computational cost in the network design stage. This paper presents our lightweight feature extraction network architecture for object detection, named PVANET, which achieves real-time object detection performance without losing accuracy compared to the other state-of-the-art systems: • Computational cost: 7.9GMAC for feature extraction with 1065x640 input (cf. ResNet-101 [1]: 80.5GMAC1 ) • Runtime performance: 750ms/image (1.3FPS) on Intel i7-6700K CPU with a single core; 46ms/image (21.7FPS) on NVIDIA Titan X GPU • Accuracy: 81.8% mAP on VOC-2007; 82.5% mAP on VOC-2012 (2nd place) 1 ResNet-101 used multi-scale testing without mentioning additional computation cost. If we take this into account, ours requires only