2022
DOI: 10.1111/cgf.14692
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TogetherNet: Bridging Image Restoration and Object Detection Together via Dynamic Enhancement Learning

Abstract: Adverse weather conditions such as haze, rain, and snow often impair the quality of captured images, causing detection networks trained on normal images to generalize poorly in these scenarios. In this paper, we raise an intriguing question -if the combination of image restoration and object detection, can boost the performance of cutting-edge detectors in adverse weather conditions. To answer it, we propose an effective yet unified detection paradigm that bridges these two subtasks together via dynamic enhanc… Show more

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Cited by 16 publications
(4 citation statements)
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“…We also see that ODFC-YOLO surpasses all multi-task learning approaches. Although TogetherNet [51] obtains competitive results in two out of five categories, our method still outperforms it and achieves a 2% improvement in mAP. This proves that enhancing the feature consistency of different tasks is more conducive to the improvement of detection performance.…”
Section: Compare With Sota Methodsmentioning
confidence: 85%
See 2 more Smart Citations
“…We also see that ODFC-YOLO surpasses all multi-task learning approaches. Although TogetherNet [51] obtains competitive results in two out of five categories, our method still outperforms it and achieves a 2% improvement in mAP. This proves that enhancing the feature consistency of different tasks is more conducive to the improvement of detection performance.…”
Section: Compare With Sota Methodsmentioning
confidence: 85%
“…Multi-task learning methods [50][51][52] aimed to learn multiple related tasks simultaneously, benefiting other tasks through the knowledge gained from one task. One popular method is the Task Relation Network [52] (TRN), which built explicit relationship between tasks from a statistical perspective.…”
Section: Multi-task Learningmentioning
confidence: 99%
See 1 more Smart Citation
“…However, these approaches often struggle to adapt to various image types, and the adjustment of enhancement parameters heavily relies on manual intervention, leading to suboptimal results. Building on these foundations, some research has integrated image enhancement with object detection [23,24], proposing the Adaptive Enhancement Model for Object Detection Network (ARODNet) [25] to improve detection performance under adverse conditions [26,27]. Nonetheless, these methods do not optimize the structure of the detection network itself, limiting their effectiveness during training.…”
Section: Introductionmentioning
confidence: 99%