An Ensemble Model for Distorted Images in Real Scenarios
Boyuan Ji Jianchang Huang Wenzhuo Huang Shuke He
Image acquisition conditions and environments can significantly affect
high-level tasks in computer vision, and the performance of most computer
vision algorithms will be limited when trained on distortion-free datasets.
Even with updates in hardware such as sensors and deep learning methods, it
will still not work in the face of variable conditions in real-world
applications. In this paper, we apply the object detector YOLOv7 to detect
distorted images from the dataset CDCOCO. Through carefully designed
optimizations including data enhancement, detection box ensemble, denoiser
ensemble, super-resolution models, and transfer learning, our model achieves
excellent performance on the CDCOCO test set. Our denoising detection model can
denoise and repair distorted images, making the model useful in a variety of
real-world scenarios and environments.