Open Access

ARTICLE

Faster RCNN Target Detection Algorithm Integrating CBAM and FPN

Wenshun Sheng^*, Xiongfeng Yu, Jiayan Lin, Xin Chen

School of Computer and Communication Engineering, PuJiang Institute, Nanjing Tech University, Nanjing, 211200, China

* Corresponding Author: Wenshun Sheng. Email:

Computer Systems Science and Engineering 2023, 47(2), 1549-1569. https://doi.org/10.32604/csse.2023.039410

Received 27 January 2023; Accepted 10 March 2023; Issue published 28 July 2023

Abstract

Small targets and occluded targets will inevitably appear in the image during the shooting process due to the influence of angle, distance, complex scene, illumination intensity, and other factors. These targets have few effective pixels, few features, and no apparent features, which makes extracting their efficient features difficult and easily leads to false detection, missed detection, and repeated detection, affecting the performance of target detection models. An improved faster region convolutional neural network (RCNN) algorithm (CF-RCNN) integrating convolutional block attention module (CBAM) and feature pyramid networks (FPN) is proposed to improve the detection and recognition accuracy of small-size objects, occluded or truncated objects in complex scenes. Firstly, the CBAM mechanism is integrated into the feature extraction network to improve the detection ability of occluded or truncated objects. Secondly, the FPN-featured pyramid structure is introduced to obtain high-resolution and vital semantic data to enhance the detection effect of small-size objects. The experimental results show that the mean average precision of target detection of the improved algorithm on PASCAL VOC2012 is improved to 76.1%, which is 13.8 percentage points higher than that of the commonly used Faster RCNN and other algorithms. Furthermore, it is better than the commonly used small sample target detection algorithm.

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Keywords

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