Small-Size Target Detection in Remotely Sensed Image Using Improved Multi-Scale Features and Attention Mechanism

Abstract

Object detection for remote sensing images has problems such as complex backgrounds, multi-scale and difficulty in the detection of small targets. Because of the above problems, an improved object detection algorithm for remote-sensing images is proposed. Firstly, fusing the characteristics of shallow and deep networks, the original feature pyramid structure is reconstructed, the improved adaptive feature fusion structure is introduced before the network prediction, and the location and category information of different feature maps are fused. Secondly, the attention mechanism is introduced into the network to reduce the interference caused by complex backgrounds; Finally, the up-sampling module is improved to expand the receptive field and realize up-sampling based on semantic information. Through experiments, we show that our network achieves 93.9% and 98.5% detection accuracy on Levir and RSOD dataset, respectively, with the detection speed, reaching 98.26fps. Compared with the original network, the accuracy is increasing by 4% and 1.2%, respectively. Experimental results show that the detection accuracy of our algorithm is improved while maintaining the light weights and efficiencies of the original network.

Fusing Global and Local Information Network for Tassel Detection in UAV Imagery

“…Inevitably, such large strides tend to compress the feature information of small objects into small points or even make them disappear in low-resolution feature maps due to pixel limitations. A common solution to this challenge is to introduce additional branches that fuse multiscale information with the output feature set [15], [16], [17]. Whereas, this approach is not always efficient because there are distinct semantic differences between different layers.…”