LightR-YOLOv5: A compact rotating detector for SARS-CoV-2 antigen-detection rapid diagnostic test results

Rongsheng Wang; Yaofei Duan; Menghan Hu; Xiaohong Liu; Yukun Li; Qinquan Gao; Tong Tong; Tao Tan

doi:10.1016/j.displa.2023.102403

LightR-YOLOv5: A compact rotating detector for SARS-CoV-2 antigen-detection rapid diagnostic test results

Displays. 2023 Jul:78:102403. doi: 10.1016/j.displa.2023.102403. Epub 2023 Mar 14.

Authors

Rongsheng Wang¹, Yaofei Duan¹, Menghan Hu², Xiaohong Liu³, Yukun Li¹, Qinquan Gao⁴, Tong Tong⁴, Tao Tan¹

Affiliations

¹ Faculty of Applied Sciences, Macao Polytechnic University, Rua de Luís Gonzaga Gomes, 999078, Macao Special Administrative Region of China.
² Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200240, China.
³ John Hopcroft Center, Shanghai Jiao Tong University, Shanghai 200240, China.
⁴ College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China.

Abstract

Nucleic acid testing is currently the golden reference for coronaviruses (SARS-CoV-2) detection, while the SARS-CoV-2 antigen-detection rapid diagnostic tests (RDT) is an important adjunct. RDT can be widely used in the community or regional screening management as self-test tools and may need to be verified by healthcare authorities. However, manual verification of RDT results is a time-consuming task, and existing object detection algorithms usually suffer from high model complexity and computational effort, making them difficult to deploy. We propose LightR-YOLOv5, a compact rotating SARS-CoV-2 antigen-detection RDT results detector. Firstly, we employ an extremely light-weight L-ShuffleNetV2 network as a feature extraction network with a slight reduction in recognition accuracy. Secondly, we combine semantic and texture features in different layers by judiciously combining and employing GSConv, depth-wise convolution, and other modules, and further employ the NAM attention to locate the RDT result detection region. Furthermore, we propose a new data augmentation approach, Single-Copy-Paste, for increasing data samples for the specific task of RDT result detection while achieving a small improvement in model accuracy. Compared with some mainstream rotating object detection networks, the model size of our LightR-YOLOv5 is only 2.03MB, and it is 12.6%, 6.4%, and 7.3% higher in mAP@.5:.95 metrics compared to RetianNet, FCOS, and R³Det, respectively.

Keywords: Lightweight; RDT detection; Rotating object detection; YOLOv5.