Open Access

ARTICLE

Improving Real-Time Animal Detection Using Group Sparsity in YOLOv8: A Solution for Animal-Toy Differentiation

Zia Ur Rehman¹, Ahmad Syed^2,*, Abu Tayab³, Ghanshyam G. Tejani^4,5,*, Doaa Sami Khafaga⁶, El-Sayed M. El-kenawy^7,8

1 College of Information and Artificial Intelligence, Yangzhou University, Yangzhou, 225009, China
2 School of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, China
3 School of Mechanical Engineering, Yanshan University, Qinhuangdao, 066004, China
4 Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, 600077, India
5 Applied Science Research Center, Applied Science Private University, Amman, 11937, Jordan
6 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
7 Department of Programming, School of Information and Communications Technology (ICT), Bahrain Polytechnic, Isa Town, P.O.Box 33349, Bahrain
8 Jadara Research Center, Jadara University, Irbid, 21110, Jordan

* Corresponding Authors: Ahmad Syed. Email: ; Ghanshyam G. Tejani. Email:

(This article belongs to the Special Issue: Advances in Image Recognition: Innovations, Applications, and Future Directions)

Computers, Materials & Continua 2026, 86(2), 1-25. https://doi.org/10.32604/cmc.2025.070310

Received 13 July 2025; Accepted 11 October 2025; Issue published 09 December 2025

Abstract

Object detection, a major challenge in computer vision and pattern recognition, plays a significant part in many applications, crossing artificial intelligence, face recognition, and autonomous driving. It involves focusing on identifying the detection, localization, and categorization of targets in images. A particularly important emerging task is distinguishing real animals from toy replicas in real-time, mostly for smart camera systems in both urban and natural environments. However, that difficult task is affected by factors such as showing angle, occlusion, light intensity, variations, and texture differences. To tackle these challenges, this paper recommends Group Sparse YOLOv8 (You Only Look Once version 8), an improved real-time object detection algorithm that improves YOLOv8 by integrating group sparsity regularization. This adjustment improves efficiency and accuracy while utilizing the computational costs and power consumption, including a frame selection approach. And a hybrid parallel processing method that merges pipelining with dataflow strategies to improve the performance. Established using a custom dataset of toy and real animal images along with well-known datasets, namely ImageNet, MSCOCO, and CIFAR-10/100. The combination of Group Sparsity with YOLOv8 shows high detection accuracy with lower latency. Here provides a real and resource-efficient solution for intelligent camera systems and improves real-time object detection and classification in environments, differentiating between real and toy animals.

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