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

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.