Image Retrieval Method of Bayonet Vehicle Based on the Improvement of Deep Learning Network

Abstract

Aiming at the problems of low accuracy, slow calculation speed, large storage space and difficult to detect multiple targets in the search of existing bayonet vehicles, a multi-target staged image retrieval method based on Faster R-CNN preprocessing was proposed. First, the selective search network is used to obtain the probability vectors in the picture; then, the image compact semantic hash code is used to perform fingerprint encoding to quickly compare and narrow the range to obtain a range candidate pool; finally, the image to be retrieved is compared to the image in the pool Quickly compare quantized hash matrices, and use voting to select the most similar images from the pool as the output. The experimental results show that the design can achieve end-to-end training. The average accuracy rate (0.829) and retrieval response time (0.698s) are significantly improved compared to the conventional hash-based retrieval method on the BIT-Vehicle dataset. This meets the era of big data Image retrieval needs.