A Target Detection Algorithm for Remote Sensing Images Based on Deep Learning

Abstract

In order to improve the accuracy of remote sensing image target detection, this paper proposes a remote sensing image target detection algorithm DFS based on deep learning. Firstly, dimension clustering module, loss function, and sliding window segmentation detection are designed. The data set used in the experiment comes from GoogleEarth, and there are 6 types of objects: airplanes, boats, warehouses, large ships, bridges, and ports. Training set, verification set, and test set contain 73490 images, 22722 images, and 2138 images, respectively. It is assumed that the number of detected positive samples and negative samples is A and B, respectively, and the number of undetected positive samples and negative samples is C and D, respectively. The experimental results show that the precision-recall curve of DFS for six types of targets shows that DFS has the best detection effect for bridges and the worst detection effect for boats. The main reason is that the size of the bridge is relatively large, and it is clearly distinguished from the background in the image, so the detection difficulty is low. However, the target of the boat is very small, and it is easy to be mixed with the background, so it is difficult to detect. The MAP of DFS is improved by 12.82%, the detection accuracy is improved by 13%, and the recall rate is slightly decreased by 1% compared with YOLOv2. According to the number of detection targets, the number of false positives (FPs) of DFS is much less than that of YOLOv2. The false positive rate is greatly reduced. In addition, the average IOU of DFS is 11.84% higher than that of YOLOv2. For small target detection efficiency and large remote sensing image detection, the DFS algorithm has obvious advantages.

Research on detecting moving targets with an improved Kalman filter algorithm

“…For example, Guo Yan proposed a fusion algorithm for a stadium location, which uses a selfoptimizing particle filter to combine an improved algorithm for athlete trajectory estimation with a Wifi localization fingerprint algorithm to localize the stadium and then achieve object detection through image recognition [14]. Additionally, Luis Ruiz Suarez et al, "proposed a deep learning-based object detection algorithm in remote sensing images, where the image background needs to be distinguished to achieve accurate recognition results in video surveillance [15]. Yang uses a background subtraction algorithm to detect and recognize moving targets [16].…”

Optimized Approach for Image Design Processing in Optical Networks: A Comparative Study

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