Background:
Power line inspection is essential to ensure the safe and stable operation of
the power system. Object detection for tower equipment can significantly improve inspection efficiency. However, due to the low resolution of small targets and limited features, the detection accuracy of
small targets is not easy to improve.
Objective:
This study aimed to improve the tiny targets’ resolution while making the small target's texture and detailed features more prominent to be perceived by the detection model.
Methods:
In this paper, we propose an algorithm that employs generative adversarial networks to improve small objects' detection accuracy. First, the original image is converted into a super-resolution
one by a super-resolution reconstruction network (SRGAN). Then the object detection framework
Faster RCNN is utilized to detect objects on the super-resolution images.
Result:
The experimental results on two small object recognition datasets show that the model proposed in this paper has good robustness. It can especially detect the targets missed by Faster RCNN,
which indicates that SRGAN can effectively enhance the detailed information of small targets by improving the resolution.
Conclusion:
We found that higher resolution data is conducive to obtaining more detailed information
of small targets, which can help the detection algorithm achieve higher accuracy. The small object detection model based on the generative adversarial network proposed in this paper is feasible and more
efficient. Compared with Faster RCNN, this model has better performance on small object detection.
Small Object Detection from Remote Sensing Images with the Help of Object-Focused Super-Resolution Using Wasserstein GANs
