Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime (1810.02575v1)

Abstract: This work addresses the problem of semantic image segmentation of nighttime scenes. Although considerable progress has been made in semantic image segmentation, it is mainly related to daytime scenarios. This paper proposes a novel method to progressive adapt the semantic models trained on daytime scenes, along with large-scale annotations therein, to nighttime scenes via the bridge of twilight time -- the time between dawn and sunrise, or between sunset and dusk. The goal of the method is to alleviate the cost of human annotation for nighttime images by transferring knowledge from standard daytime conditions. In addition to the method, a new dataset of road scenes is compiled; it consists of 35,000 images ranging from daytime to twilight time and to nighttime. Also, a subset of the nighttime images are densely annotated for method evaluation. Our experiments show that our method is effective for model adaptation from daytime scenes to nighttime scenes, without using extra human annotation.

• The paper proposes a gradual adaptation strategy that bridges the domain gap using twilight images as an intermediary for nighttime segmentation.
• It employs progressive self-learning where a daytime-trained model is fine-tuned with twilight data, significantly improving segmentation accuracy.
• Using the Nighttime Driving dataset and RefineNet baseline, the method demonstrates enhanced mean IoU performance critical for robust autonomous driving.

Semantic Image Segmentation: From Daytime Robustness to Nighttime Model Adaptation

The paper "Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime" presents an approach for addressing the challenges inherent in adapting semantic image segmentation models developed predominantly for daytime scenes to nighttime environments. While significant advances have been made in the field of semantic image segmentation, particularly in scenarios with optimal lighting, this paper ventures into the less-explored topic of nighttime scene understanding. This exploration holds particular relevance for autonomous driving applications, where dependable object recognition remains vital despite varying illumination conditions.

Methodology

The methodology proposed involves a progressive adaptation strategy of semantic models originally trained on extensive annotated daytime datasets. This adaptation leverages the transitional period of twilight, serving as an intermediary domain between daytime and nighttime scenes. The underlying hypothesis is that the domain discrepancy can be incrementally minimized by bridging through twilight images, which inherently possess intermediate levels of natural lighting conditions compared to daytime and nighttime.

The paper implements a gradual model adaptation process entailing several stages:

1. Daytime Model Training: An initial semantic segmentation model is trained using daytime images with human annotations.
2. Twilight Time Application: The trained model is then applied to twilight time images, aligning with three twilight subcategories: civil, nautical, and astronomical twilight.
3. Progressive Self-Learning: The twilight time images, along with their generated labels, are used to fine-tune the model through a self-learning mechanism, creating intermediate adaptation steps until the model is eventually suited for nighttime images.

The methodological approach capitalizes on the availability of large-scale annotated data for daytime images, progressing to unlabelled images generated via intermediate steps. This effectively circumvents the cost-intensive process of collecting and annotating diverse nighttime images.

Datasets

Contributing to the robustness of the methodology, the paper introduces the Nighttime Driving dataset, comprising 35,000 images, spanning from daytime through twilight to nighttime. The dataset includes 50 densely annotated images for evaluation purposes. This sequential arrangement of images supplies a comprehensive spectrum of lighting conditions, thereby facilitating model adaptation through varied environmental conditions.

Experimental Evaluation and Results

The experimental analysis utilizes RefineNet, a well-established segmentation model, as the baseline for adaptation. Evaluation against several adaptation steps showcased that the stepwise methodological approach not only mitigates domain gaps progressively but also enhances segmentation performance significantly compared to direct application models or one-step adaptation approaches.

Quantitatively, the gradual method yields notable improvements in mean IoU (Intersection over Union) benchmarks over approaches leveraging direct daytime models on nighttime images, as well as over single, abrupt adaptation steps. Specifically, the refined progressive model consistently outperformed its counterparts across various segmentation tasks, underscoring the efficacy of this progressive domain adaptation approach.

Implications and Future Work

The proposed framework offers considerable promise for practical applications, particularly within autonomous vehicle systems that necessitate reliable object detection across fluctuating lighting conditions. The insights gleaned from this paper emphasize the necessity for adaptive models that are robust against environmental variances. Theoretically, the application of incremental transfer learning paradigms as an architectural scaffold highlights potential future work in unsupervised and semi-supervised learning methodologies which could further refine adaptation processes in similar tasks.

Looking ahead, merging limited targeted manual annotations with automated adaptation methods for well-defined challenging environments, like nighttime scenes, might enhance the fidelity of segmentation tasks. Further efforts may also include addressing indeterminate regions in nighttime imagery, potentially classifying such areas as distinct segments needing specialized processing.

In conclusion, this paper effectively scaffolds a transition from daytime segmentation models to nighttime applicability, setting a foundational framework for future research in adaptive model trainings within varied, real-world environments.