Open Access

ARTICLE

YOLOv2PD: An Efficient Pedestrian Detection Algorithm Using Improved YOLOv2 Model

Chintakindi Balaram Murthy¹, Mohammad Farukh Hashmi¹, Ghulam Muhammad^2,3,*, Salman A. AlQahtani^2,3

1 Department of Electronics and Communication Engineering, National Institute of Technology, Warangal, 506004, India
2 Research Chair of Pervasive and Mobile Computing, King Saud University, Riyadh, 11543, Saudi Arabia
3 Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia

* Corresponding Author: Ghulam Muhammad. Email:

(This article belongs to this Special Issue: Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)

Computers, Materials & Continua 2021, 69(3), 3015-3031. https://doi.org/10.32604/cmc.2021.018781

Received 21 March 2021; Accepted 04 May 2021; Issue published 24 August 2021

Abstract

Real-time pedestrian detection is an important task for unmanned driving systems and video surveillance. The existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely distributed pedestrians by losing some of their detection accuracy in such cases. Therefore, the proposed algorithm YOLOv2 (“YOU ONLY LOOK ONCE Version 2”)-based pedestrian detection (referred to as YOLOv2PD) would be more suitable for detecting smaller and densely distributed pedestrians in real-time complex road scenes. The proposed YOLOv2PD algorithm adopts a Multi-layer Feature Fusion (MLFF) strategy, which helps to improve the model’s feature extraction ability. In addition, one repeated convolution layer is removed from the final layer, which in turn reduces the computational complexity without losing any detection accuracy. The proposed algorithm applies the K-means clustering method on the Pascal Voc-2007+2012 pedestrian dataset before training to find the optimal anchor boxes. Both the proposed network structure and the loss function are improved to make the model more accurate and faster while detecting smaller pedestrians. Experimental results show that, at image resolution, the proposed model achieves 80.7% average precision (AP), which is 2.1% higher than the YOLOv2 Model on the Pascal Voc-2007+2012 pedestrian dataset. Besides, based on the experimental results, the proposed model YOLOv2PD achieves a good trade-off balance between detection accuracy and real-time speed when evaluated on INRIA and Caltech test pedestrian datasets and achieves state-of-the-art detection results.

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