An Ensemble Model for Distorted Images in Real Scenarios

TL;DR

This paper presents an ensemble approach combining data enhancement, denoising, super-resolution, and transfer learning with YOLOv7 to improve detection of distorted images in real-world scenarios.

Contribution

It introduces a novel ensemble model that effectively denoises and detects distorted images, enhancing real-world computer vision applications.

Findings

High detection accuracy on CDCOCO dataset

Effective denoising and image repair capabilities

Robust performance across variable real-world conditions

Abstract

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.