A PSO and BFO-Based Learning Strategy Applied to Faster R-CNN for Object Detection in Autonomous Driving

Abstract

Recently outstanding object detection results are achieved by the faster region-based convolutional network (faster R-CNN). Particularly, high-quality detection proposals are obtained by the region proposal network (RPN). Nevertheless, part of the parameters in RPN is assigned by prior knowledge. Therefore the underfitting problem is likely to appear on the training model of RPN. In other words, the generalization ability of RPN is not enough. Increasing parameters is an effective solution to this problem. Thereupon a strengthened RPN (SRPN) is designed to expand the exploring space of RPN. Acquiring the optimal parameter values of SRPN is a non-deterministic polynomial-time hard problem, which can be solved by swarm intelligence algorithms. Thereafter a particle swarm optimization (PSO) and bacterial foraging optimization (BFO)-based learning strategy (PBLS) is introduced to optimize the classifier and loss function of SRPN. In SRPN, a novel multi-level extracting network is created to improve the feature sampling ability. Moreover, the mathematical model of the smooth L1 loss function is improved to boost the fitting ability. Additionally, support vector machine (SVM) method is applied to enhance the classifier learning capability. PBLS is applied to SRPN (PBLS_SRPN). The parameters of SVM and the improved loss function are optimized by the BFO and PSO methods, respectively. Then, the performance of SRPN is further promoted. The excellent results are obtained by our proposed methods on PASCAL VOC 2007, 2012, MS COCO, and KITTI data sets. Consequently, PBLS_SRPN is effective for object detection in autonomous driving.