Title: A novel lightweight blue sheep target real-time detection algorithm

Authors: Xin Wang; Tao Wang; Rui Wu; Ying Chao Niu; Qian Ji; Wei Shi

Addresses: School of Information Engineering, Ningxia University, Yinchuan, 750021, China; School of Information Engineering, Lanzhou Vocational Technical, Lanzhou, 730070, China ' School of Information Engineering, Ningxia University, Yinchuan, 750021, China ' School of Information Engineering, Ningxia University, Yinchuan, 750021, China ' School of Information Engineering, Lanzhou Vocational Technical, Lanzhou, 730070, China ' School of Information Engineering, Ningxia University, Yinchuan, 750021, China ' School of Information Engineering, Ningxia University, Yinchuan, 750021, China

Abstract: The utilisation of advanced algorithms in detecting blue sheep populations offers significant real-time statistical data for blue sheep protection. However, numerous classical algorithms employ convolution structures as fundamental units for data processing, resulting in a considerable number of redundant channel calculations and a lack of a holistic understanding of the image characteristics. Addressing these challenges, this paper maximises the complementary advantages of convolution and involution. It integrates a learnable weighted splicing structure to construct a lightweight real-time network model, reducing computational complexity to a certain extent and ensuring space efficiency. The model considers both global and local information of the feature map. Experiments demonstrate that the detection speed of this method is 38.3% faster than YOLOv5, showcasing improved realtime performance. Moreover, the AP50 is 0.07 higher than YOLOv5, and the generated model capacity is 85% of YOLOv5.

Keywords: blue sheep; YOLO; You Only Look Once; target detection; convolution; involution.

DOI: 10.1504/IJCSM.2024.142732

International Journal of Computing Science and Mathematics, 2024 Vol.20 No.3, pp.208 - 227

Accepted: 19 Jul 2024
Published online: 19 Nov 2024 *

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