Building improved object detection algorithm based on feature pyramid network

In order to improve the accuracy and efficiency of object detection, this paper proposes an artificial intelligence algorithm for object detection, which combines the advantages of deep learning and traditional image processing technology to solve the difficult problem of object detection in complex scenes. Firstly, this paper systematically reviews the relevant literatures in the field of target detection, and analyzes the advantages and disadvantages of the existing algorithms and their application scenarios. On this basis, the selection and pre-processing of data set and the specific ideas and design details of algorithm improvement are described in detail. At the same time, in order to verify the effectiveness of the algorithm, the experimental environment and parameters were designed, and a large number of experimental tests were carried out. Experimental results show that compared with traditional target detection algorithms, the proposed algorithm has significant advantages in detection accuracy and can identify and locate targets more accurately.