Light and Small UAV Image Target Detection Based on YOLOv8 Algorithm and Experimental Reform of University Physics Course

This research systematically discusses the technical advantages and application value of YOLOv8 deep learning algorithm in the field of object detection in light and small UAV images. Aiming at the technical difficulties such as small target scale and complex background in UAV aerial shooting scenes, the experimental results show that YOLOv8 algorithm can achieve real-time processing speed of 63FPS while ensuring the detection accuracy of 98.6% mAP (average accuracy mean). The multi-scale feature fusion mechanism and lightweight network design effectively balance the contradiction between detection accuracy and computation efficiency. Based on this technological breakthrough, the research further constructed an innovative application framework for the university physics experiment curriculum reform: By transforming UAV dynamic target detection technology into a "problem-oriented" experimental teaching carrier, comprehensive experimental projects integrating machine vision, automatic control and physical principles can be developed, effectively breaking the discipline barriers in traditional experimental teaching, and building a complete practice chain of "theoretical modeling - algorithm realization - hardware verification". This teaching mode driven by real engineering problems can not only visually verify physical laws through visual test results, but also cultivate students' interdisciplinary system thinking through the algorithm tuning process, and establish a multi-dimensional experimental evaluation system based on test performance indicators.