Drug recognition detection based on deep learning and improved YOLOv8

Abstract

Identifying drugs from surveillance or other videos presents challenges such as small target sizes, class imbalance, and similarities to other objects. Additionally, the hardware used to capture videos and the video resolution and clarity limit model scalability, leading to poor detection accuracy in traditional models. To address this issue, we propose an improved YOLOv8s-based model. The experimental outcomes reveal that the improved YOLOv8s model attains a precision of 95.1% and a mAP@50 of 87.4% in drug detection and identification, representing improvements of 3.0% and 2.2% over the original YOLOv8s model. The proposed improvements to YOLOv8s effectively boost detection accuracy and recognition rates while preserving high efficiency. This model demonstrates superior overall detection performance compared to other algorithms, providing fresh perspectives and methods for advancing research and applications in drug detection and recognition.