Research on Optimization of Computer Vision Object Detection Based on Deep Learning
This paper conducts an in-depth study on the application of deep learning in computer vision object detection, focusing on how to optimize the object detection algorithm to improve its performance and efficiency. The study first reviews the development of the field of object detection and analyzes the advantages and disadvantages of traditional methods and deep learning methods. Subsequently, several mainstream deep learning object detection frameworks, including R-CNN series, YOLO series and SSD, are introduced in detail, and their principles, characteristics and performance are compared. On this basis, this paper proposes an improved object detection algorithm, which effectively improves the detection accuracy and speed by introducing attention mechanism and multi-scale feature fusion strategy. Experimental results show that the algorithm has achieved excellent performance on public datasets such as Pascal VOC and Coco, with an average accuracy improvement of 3.5% and an inference speed improvement of 15%. In addition, this paper also discusses the challenges faced by object detection in practical applications, such as small object detection, dense object detection, etc., and proposes corresponding optimization strategies. Finally, the future development direction of object detection technology is prospected, providing a reference for subsequent research.