Task-Driven Super Resolution: Object Detection in Low-resolution Images (1803.11316v1)

Abstract: We consider how image super resolution (SR) can contribute to an object detection task in low-resolution images. Intuitively, SR gives a positive impact on the object detection task. While several previous works demonstrated that this intuition is correct, SR and detector are optimized independently in these works. This paper proposes a novel framework to train a deep neural network where the SR sub-network explicitly incorporates a detection loss in its training objective, via a tradeoff with a traditional detection loss. This end-to-end training procedure allows us to train SR preprocessing for any differentiable detector. We demonstrate that our task-driven SR consistently and significantly improves accuracy of an object detector on low-resolution images for a variety of conditions and scaling factors.

• The paper introduces Task-Driven Super Resolution (TDSR), an end-to-end framework that co-trains super-resolution and object detection networks by incorporating a detection loss.
• TDSR addresses the limitations of traditional super-resolution methods, which prioritize visual quality over task-specific performance, by aligning image enhancement with machine-based object detection needs.
• Evaluations on PASCAL VOC show TDSR significantly improves object detection performance (e.g., mAP up to 62.2% at 4 imes degradation) compared to baseline and existing SR methods, demonstrating potential for integrated learning in vision AI.

Task-Driven Super Resolution: Object Detection in Low-resolution Images

The paper authored by Haris et al. presents a sophisticated framework aimed at enhancing object detection in low-resolution images through a novel form of super-resolution (SR), named Task-Driven Super Resolution (TDSR). Unlike traditional methods that independently optimize SR and object detection networks, this research integrates the objectives of SR with that of object detection, allowing both to be co-trained end-to-end. Therefore, the proposed framework introduces a detection loss into the SR network's learning objective, facilitating the development of a preprocessing module tailored for any differentiable object detector.

In typical scenarios, SR seeks to reconstruct high-resolution images from low-resolution counterparts, evaluated through metrics such as PSNR and SSIM. Though these methods have been used to improve image clarity, they often fail to address the specific needs of downstream tasks such as object detection. This investigation highlights that a simple, direct enhancement does not necessarily translate into better detection results, as SR traditionally prioritizes visual quality over functional accuracy for machine-based tasks.

The proposed TDSR approach hinges on two key insights. Firstly, the SR problem is inherently ill-posed since multiple high-resolution images can be compressed to yield the same low-resolution image, making prior approaches inadequate when evaluated for task-specific outcomes such as detection. Secondly, human perception of image quality diverges significantly from machine perception, necessitating an SR design that aligns with machine tasks. The TDSR model incorporates these insights by introducing a compound loss function that combines traditional SR objectives with detection performance, weighed through alpha (α) and beta (β) coefficients respectively.

Extensive experimental evaluations are conducted on the PASCAL VOC datasets with degradation factors of $4\times$ and $8\times$. The results consistently demonstrate superior object detection performance using TDSR when compared against both baseline SR methods and existing high-performance approaches like SRGAN. For example, mAP scores with TDSR reach 62.2% for $4\times$ and 37.5% for $8\times$, marking considerable improvements over other competing methods, which also suffer from a drop in detection accuracy, particularly in noisy or blurred conditions.

The implications of this research are substantial for both the theoretical development and practical deployment of vision systems in challenging scenarios where high-resolution training data may not be readily available or acquisition costs are prohibitive. By demonstrating that task-specific objectives can substantially enhance the efficacy of SR for machine vision tasks, this work encourages future exploration of SR in tandem with a host of vision applications, such as segmentation and captioning, to uncover potentially generalizable methodologies across the spectrum of visual computing tasks.

Moreover, this paper suggests a meaningful direction for future research in refining the relationship between image preprocessing and downstream analytics, opening doors to entirely new frameworks of integrated learning in vision AI. This pursuit holds promise for unveiling more robust and adaptable AI systems capable of efficient operation under a diverse range of vision-related tasks and conditions.