Title: Medical personal protective equipment detection based on attention mechanism and multi-scale fusion

Authors: Jianlou Lou; Xiangyu Li; Guang Huo; Feng Liang; Zhaoyang Qu; Tianrui Lou; Ndagijimana Kwihangano Soleil

Addresses: School of Computer Science, Northeast Electric Power University, Jilin 132012, China ' School of Computer Science, Northeast Electric Power University, Jilin 132012, China ' School of Computer Science, Northeast Electric Power University, Jilin 132012, China ' School of Computer Science, Northeast Electric Power University, Jilin 132012, China ' School of Computer Science, Northeast Electric Power University, Jilin 132012, China ' Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China ' School of Computer Science, Northeast Electric Power University, Jilin 132012, China

Abstract: Deep neural networks (DNNs) have shown excellent effectiveness in object detection and greatly benefit people in various physical scenes. In this paper, we focus on a meaningful physical scene, medical personal protective equipment detection, where the performance degrades for two reasons: background information interference and different detection target scales. To solve the problems above, we propose two novel modules, a deformable and attention residual with 50 layers (DAR50) feature extraction module and a criss-cross feature pyramid network (CCFPN) feature fusion module. Concretely, the DAR50 is target morphology-aware and can enhance the feature information. The CCFPN raises the multi-scale detection performance by fusing the pixel information of the feature maps and then fusing the features of different stages. Combining the two modules, we construct a novel object detection network called attention and multi-scale fusion-based regions with convolution neural network (AMS R-CNN) features. Empirically, we prove the superiority of AMS R-CNN on a medical personal protective equipment detection dataset CPPE-5 (medical personal protective equipment) and The Visual Object Classes Challenge 2007 (VOC 2007) dataset compared with several state-of-the-art methods.

Keywords: object detection; multi-scale fusion; attention mechanism; medical personal protective equipment.

DOI: 10.1504/IJSNET.2023.129806

International Journal of Sensor Networks, 2023 Vol.41 No.3, pp.189 - 203

Received: 07 Nov 2022
Accepted: 17 Nov 2022
Published online: 30 Mar 2023 *

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