Low-resolution object detection via a lightweight super-resolution network

The object detection algorithm has developed rapidly in recent years and achieved excellent results on various benchmark datasets. However, these datasets are usually composed of high-quality [high resolution (HR), high signal-to-noise ratio, etc.] images. In many scenarios, we need to detect objects in low-resolution (LR) images. But the detector trained in HR images performs poorly in LR images. The image super-resolution (SR) algorithm is an important image processing technology that has been proven to improve the performance of various visual tasks. Based on this consideration, we combine image SR technology with the object detection task to design an object detection algorithm for detecting LR images. Specifically, we propose a lightweight SR algorithm that achieves a good balance between parameters and performance. We perform SR reconstruction on the LR image in advance and detect the reconstructed image instead of directly detecting the LR image. Both quantitative and qualitative experimental results show that our LR object detection algorithm significantly improves the detection performance of LR images.