An Ensemble Model for Distorted Images in Real Scenarios
This work addresses the challenge of improving computer vision algorithms for distorted images in real-world applications, though it appears incremental as it builds on existing methods like YOLOv7.
The paper tackled the problem of object detection in distorted images from real-world scenarios by applying YOLOv7 to the CDCOCO dataset with optimizations like data enhancement and ensemble techniques, achieving excellent performance on the test set.
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.