Object Detection Algorithm and Challenges

Object detection, a core task in computer vision, involves identifying and localizing objects in images or videos. Recent deep learning advances have significantly improved accuracy and speed. This chapter explores traditional two-stage methods and modern one-stage techniques. The chapter begins by tracing the history of deep learning and its pivotal role in advancing object detection, followed by a discussion of performance metrics used to evaluate detection accuracy and inference time. A detailed examination of the YOLO series, from its inception to the latest iteration, YOLOv8, highlights the architectural innovations and contributions of each version. Additionally, the chapter addresses the significance of backbone networks and benchmark datasets in driving research progress. Key challenges in the field, including scale and class imbalance, are also analyzed. The chapter concludes by identifying recent trends and future research directions, offering a comprehensive resource for understanding the current state and potential applications of object detection technologies.