Exploration on Computer Vision Object Detection Algorithms Under Artificial Intelligence

In computer vision object detection, traditional detection methods often have poor performance when dealing with a large amount of background interference, target scale changes, and occlusion, and cannot capture all the complexity of the target, which will result in limited detection performance. In contrast, the application of computer vision object detection algorithms based on artificial intelligence in this field has become mainstream, driving the development of a series of efficient object detection methods. This article focuses on combining the convolutional neural network (CNN) algorithm to explore the relevant research on input image and candidate region selection, feature extraction and region classification, as well as optimization and evaluation indicators. Simulation experiments have concluded that compared to traditional visual detection, the improved accuracy index is improved by about 9.3%, while the recall performance index is improved by 20.5%. This study has improved the accuracy and efficiency of object detection, enabling computers to more reliably understand and interact with the real world, thus providing a solid foundation for automation and intelligent applications, with the potential to improve quality of life and promote technological innovation.