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Object Detection Under Rainy Conditions for Autonomous Vehicles: A Review of State-of-the-Art and Emerging Techniques

Mazin Hnewa, Hayder Radha

arXiv:2006.16471v4cs.CV

TL;DR

Rain can substantially degrade autonomous-vehicle object detection, while the impact of rain remains comparatively understudied. The paper surveys detection, deraining, image translation, and domain adaptation techniques and evaluates their use under rainy conditions. Its central conclusion is that current approaches do not adequately mitigate realistic, diverse weather challenges, despite some promise from generative translation and domain adaptation.

  • Problem

    Rain degrades visual object detection for autonomous vehicles, but its impact and mitigation remain insufficiently studied in autonomous-driving contexts.

  • Method

    The paper surveys and experimentally evaluates object detectors, deraining methods, unsupervised image translation, and deep-learning domain adaptation for rainy conditions.

  • Results

    Current image translation and domain adaptation approaches show some promise, but existing solutions do not adequately mitigate realistic challenges across diverse weather conditions.

  • Takeaways & Limitations

    The findings highlight the need for new paradigms and more representative rainy-condition data for autonomous-driving research.

  • Takeaways & Limitations

    Real rainy-condition datasets are few, often inadequately annotated, and do not capture the full diversity of driving rain effects.

Abstract

from arXiv · show

Advanced automotive active-safety systems, in general, and autonomous vehicles, in particular, rely heavily on visual data to classify and localize objects such as pedestrians, traffic signs and lights, and other nearby cars, to assist the corresponding vehicles maneuver safely in their environments. However, the performance of object detection methods could degrade rather significantly under challenging weather scenarios including rainy conditions. Despite major advancements in the development of deraining approaches, the impact of rain on object detection has largely been understudied, especially in the context of autonomous driving. The main objective of this paper is to present a tutorial on state-of-the-art and emerging techniques that represent leading candidates for mitigating the influence of rainy conditions on an autonomous vehicle's ability to detect objects. Our goal includes surveying and analyzing the performance of object detection methods trained and tested using visual data captured under clear and rainy conditions. Moreover, we survey and evaluate the efficacy and limitations of leading deraining approaches, deep-learning based domain adaptation, and image translation frameworks that are being considered for addressing the problem of object detection under rainy conditions. Experimental results of a variety of the surveyed techniques are presented as part of this tutorial.

I. INTRODUCTION

Rain can substantially impair visual object detection for autonomous vehicles by reducing contrast, visibility, and informative image features. The article surveys detection architectures and rain-mitigation approaches, while concluding that existing solutions remain insufficient for realistic diverse weather.

  • I. INTRODUCTION: Rain reduces scene contrast and visibility, potentially degrading detection of critical objects for autonomous vehicles.Rain is treated as a dynamic weather condition with complex visual effects.
  • I. INTRODUCTION: Raindrops block reflected light, while rain streaks increase whiteness and reduce contrast in images and video.Rain has variable sizes, shapes, speeds, and spatial distributions.
  • I. INTRODUCTION: The article surveys state-of-the-art and emerging techniques for mitigating rain’s influence on autonomous-vehicle object detection.It covers object detection, deraining, domain adaptation, and image translation frameworks.
  • I. INTRODUCTION: Current generative image translation and domain adaptation approaches show promise but do not adequately address realistic autonomous-driving challenges across diverse weather.The paper presents experimental results to motivate new paradigms for severe-weather driving.
  • I. INTRODUCTION: The tutorial reviews Faster R-CNN and YOLO as representative two-stage and direct-regression detection architectures.These architectures can degrade differently under rainy conditions and interact differently with mitigation frameworks.

A. Deep learning-based methods for object detection

The section introduces Faster R-CNN and YOLO as representative deep-learning detection architectures. Faster R-CNN uses region proposals and refinement, whereas YOLO predicts boxes and class probabilities directly from the image.

  • A. Deep learning-based methods for object detection: Faster R-CNN is a two-stage detector that identifies region proposals before refining boxes and assigning class probabilities.Its region proposal network shares convolutional features with the downstream detector.
  • A. Deep learning-based methods for object detection: Faster R-CNN generates feature maps, predicts candidate regions, removes redundant proposals with NMS, and classifies surviving regions.RoI pooling supplies regional features to separate box-regression and classification layers.
  • A. Deep learning-based methods for object detection: YOLO treats detection as regression and predicts bounding boxes and class probabilities directly from a full image in one evaluation.The image is divided into grid cells, with each cell responsible for objects whose centers fall within it.

B. Object detection performance for neural network architectures in clear and rainy conditions

The study evaluates detectors trained on clear-weather BDD100K images against real rainy conditions, using manually screened and object-balanced test sets. Synthetic rain is excluded because existing simulations do not reliably reproduce natural driving rain.

  • B. Object detection performance for neural network architectures in clear and rainy conditions: The study asks how much clear-trained neural detectors degrade when tested in rainy weather.It evaluates two major deep-learning architecture classes using visual and numerical results.
  • B. Object detection performance for neural network architectures in clear and rainy conditions: BDD100K provides 100,000 video clips and annotated frames spanning diverse geographic, environmental, and weather conditions.The study focuses on vehicles, pedestrians, traffic lights, and traffic signs, training on clear-weather images.
  • B. Object detection performance for neural network architectures in clear and rainy conditions: Synthetic rain was not used because common simulation methods fail to capture realistic rain on driving vehicles, creating a mismatch with natural rainy conditions.Synthetic rain would preserve identical scene content across clear and rainy versions, but realism was judged inadequate.
  • B. Object detection performance for neural network architectures in clear and rainy conditions: The researchers manually selected genuinely rainy BDD100K images because some frames were incorrectly tagged as rainy despite clear or cloudy conditions.Clear and rainy test sets were constructed with approximately equal numbers of annotated objects for statistical comparability.
  • B. Object detection performance for neural network architectures in clear and rainy conditions: Available weather-diverse datasets remain insufficient for reliably training object detectors, and accurate annotation of real rainy data is expensive and time-consuming.The limitation applies even to relatively well-annotated datasets such as BDD100K.

C. Performance metric

The study measures detection performance with mean average precision, which averages class-specific average precision values derived from precision–recall curves.

  • C. Performance metric: Mean average precision (mAP) is computed as the mean of average precision across all object classes.Each average precision is the numerical area under a monotonically updated precision–recall curve.
  • C. Performance metric: A detection counts as a true positive when its intersection-over-union exceeds 0.5 and its class label matches the ground truth.These conditions determine the precision–recall curve used for AP calculation.
  • C. Performance metric: The precision–recall curve is made monotonically decreasing by replacing precision at recall r with the maximum precision observed for any recall r′ > r.AP is then obtained by numerical integration.

D. Results and Discussion

Detection performance trained on clear visuals significantly degrades in rainy conditions because rain obscures important features. Pedestrians and traffic lights experience larger AP declines than vehicles and traffic signs, while deraining methods are reviewed as potential remedies.

  • D. Results and Discussion: Clear-trained Faster R-CNN and YOLO models significantly degrade when tested in rainy conditions.Rain covers and distorts visual details used to classify and localize objects; Figure 4 shows failures to perceive many objects.
  • D. Results and Discussion: Pedestrian and traffic-light AP declines more than vehicle and traffic-sign AP under rainy conditions.Vehicles occupy larger image regions, while reflective traffic signs retain salient features despite distortion.
  • D. Results and Discussion: The tutorial reviews three deep-learning deraining algorithms and their limitations when combined with object detection.The reviewed methods are DDN, DeRaindrop, and PReNet.

A. Deep Detail Network

The paper describes DDN and DeRaindrop as deep-learning methods for removing rain or raindrops from images, while Table II evaluates detection performance across clear and rainy conditions.

  • A. Deep Detail Network: DDN uses ResNet to predict the difference between clear and rainy images from high-frequency rainy-image details.It ignores the underlying scene’s low-frequency background interference when estimating the rain-related difference.
  • A. Deep Detail Network: DeRaindrop uses an attentive GAN to learn raindrop regions and their surroundings.Its Attentive-Recurrent Network produces an attention map, and a Contextual Autoencoder focuses on raindrop areas.
  • A. Deep Detail Network: Table II reports vehicle, pedestrian, traffic-light, and traffic-sign AP values together with mAP under clear and rainy conditions.The caption states that generative image translation and domain adaptation can improve performance, whereas deraining usually does not.

C. Progressive Image Deraining Network

PReNet recursively removes rain through recurrent processing, but the reported experiments show that deraining can smooth object edges and cause missed detections in rainy scenes.

  • C. Progressive Image Deraining Network: PReNet progressively removes rain across iterations using recurrent processing of the rainy image and current output.Its architecture includes residual blocks, CNN layers, and a convolutional LSTM for feature dependencies across iterations.
  • C. Progressive Image Deraining Network: The evaluated deraining models are applied as preprocessing before feeding images into object detectors.Pretrained DDN, DeRaindrop, and PReNet models process the test rainy set before detection performance is measured.
  • C. Progressive Image Deraining Network: Deraining smooths object edges, losing critical features needed for classification and localization.Figure 6 includes objects detected from rainy inputs but missed after deraining with Faster R-CNN or YOLO.
  • C. Progressive Image Deraining Network: Current deraining algorithms struggle to remove natural raindrops in realistic scenes captured by moving vehicles.Their reliance on synthetic rain and often static backgrounds creates a domain mismatch with natural rainy driving environments.

IV. ALTERNATIVE TRAINING APPROACHES FOR DEEP LEARNING BASED OBJECT DETECTION

Rainy-weather detection is constrained by limited annotated real-rain data and by poor generalization from synthetic rain. The paper therefore reviews image translation and domain adaptation using clear and unannotated rainy data.

  • IV. ALTERNATIVE TRAINING APPROACHES FOR DEEP LEARNING BASED OBJECT DETECTION: Object detectors trained on dry-weather data degrade in challenging weather, conflicting with autonomous driving’s need for reliable operation across conditions.The paper identifies the mismatch between usual training data and diverse driving weather as a central problem.
  • IV. ALTERNATIVE TRAINING APPROACHES FOR DEEP LEARNING BASED OBJECT DETECTION: Training directly on real rainy images is limited by the scarcity and poor annotation of datasets captured by moving vehicles.Available real-rain annotations are expensive and time-consuming to produce, leaving datasets inadequate for reliable deep-learning training.
  • IV. ALTERNATIVE TRAINING APPROACHES FOR DEEP LEARNING BASED OBJECT DETECTION: Synthetic-rain training generalizes poorly to real data because of domain shift between synthetic and natural rain.The paper reviews image translation and domain adaptation using annotated clear data with unannotated rainy data.

A. Unsupervised image-to-image translation

Unsupervised image-to-image translation generates rainy-domain training imagery without requiring paired clear-rainy scenes, addressing limited realistic rainy-condition data for detector training.

  • A. Unsupervised image-to-image translation: Image-to-image translation maps images between domains while preserving critical visual content and learning their joint distribution.Supervised translation uses paired same-scene examples, whereas unsupervised translation does not.
  • A. Unsupervised image-to-image translation: UNIT is used to translate clear driving images into rainy images because paired clear-rainy driving datasets are unavailable.Its training uses two independent image sets from the clear and rainy domains.
  • A. Unsupervised image-to-image translation: The UNIT generator uses a shared latent code to produce an image in the desired domain.This shared representation supports translation between the clear and rainy domains.
  • A. Unsupervised image-to-image translation: Clear annotated training images and rainy video frames are used to train UNIT and generate the train gen rainy set.The generated rainy images are then used to train object detectors evaluated on test-rain data.
  • A. Unsupervised image-to-image translation: Detection methods trained on generated rainy images are evaluated using average precision and mean average precision on rainy test data.Table II reports the performance of detectors trained with UNIT-generated rainy images.

B. Domain adaptation

Domain adaptation addresses clear-rainy domain mismatch by training Faster R-CNN to detect objects across domains using labeled clear data and unlabeled rainy data.

  • B. Domain adaptation: Domain adaptation targets the mismatch between clear and rainy domains and the lack of annotated rainy-condition training data.The reviewed framework was developed specifically for Faster R-CNN.
  • B. Domain adaptation: The reviewed domain-adaptive Faster R-CNN uses image-level and feature-level domain adaptation.An image-level classifier distinguishes global source-target attributes, while feature adaptation promotes domain-invariant representations.
  • B. Domain adaptation: The tutorial reviews domain adaptation for Faster R-CNN but reports no reviewed framework developed for YOLO.This scope reflects the authors’ stated awareness at the time of writing.
  • B. Domain adaptation: A consistency regularization stage combines the outputs of two domain classifiers to encourage agreement in their source-target decisions.The classifiers are optimized to differentiate the source and target domains.
  • B. Domain adaptation: A Gradient Reversal Layer reverses the optimization effect during backpropagation to promote domain-invariant Faster R-CNN feature maps.It acts as an identity operator in feed-forward operation while encouraging feature extraction that maximizes domain-classification error.
  • B. Domain adaptation: Training uses annotated clear-weather source images and unannotated rainy target images.The source set provides bounding-box coordinates and object categories, while the rainy set has no annotations.

C. Discussion

Image translation and domain adaptation improve several rainy-condition detection results, whereas deraining and performance on small traffic lights remain problematic across realistic scenarios.

  • C. Discussion: Deraining degrades average precision on naturally rainy scenes, while image translation and domain adaptation improve selected detection tasks.These comparisons are reported from Table II and illustrated in Figure 9.
  • C. Discussion: More than 5%: YOLO pedestrian average precision falls from around 37% in clear conditions to around 32% in rainy conditions.Image translation raises the rainy-condition value to more than 34%, narrowing the clear-rainy performance gap.
  • C. Discussion: Traffic-sign detection improves for Faster R-CNN with both image translation and domain adaptation, while image translation improves vehicle detection.These gains are reported for rainy-condition evaluation.
  • C. Discussion: Traffic-light detection with Faster R-CNN remains poor under natural rain despite domain adaptation and image translation.The clear-condition AP is only 26%, and rain effects can obscure salient features of small objects.
  • C. Discussion: The surveyed methods still leave substantial room for improvement before rainy-condition performance reaches clear-condition levels.The discussion identifies broad and diverse rainy driving scenarios as a central challenge.
  • C. Discussion: Current image translation and domain adaptation methods are not robust enough for windshield raindrops, wipers, wet-pavement reflections, mist, and splash effects.These factors create diverse rainy visual conditions represented in the test-rainy set.

V. CONCLUSION

The tutorial synthesizes limitations of current rain-mitigation techniques for moving-vehicle imagery and points to generative models as potential sources of additional training data.

  • V. CONCLUSION: The tutorial outlines state-of-the-art object detection, deraining, image translation, and domain adaptation frameworks and their rainy-weather performance.It emphasizes limitations in handling rain effects in visuals captured by moving vehicles.
  • V. CONCLUSION: The lack of annotated data capturing diverse rainy conditions for moving vehicles is identified as the most critical issue.The conclusion notes emerging efforts to collect such data using autonomous-vehicle fleets.
  • V. CONCLUSION: UNIT-based generative models may generate meaningful training data without requiring annotations.They can supplement real data and represent rainy scenarios missing from existing datasets.
  • V. CONCLUSION: Generative models could support detector training and testing by filling gaps in real annotated data and expanding scenario coverage.The conclusion presents this as a potential role rather than an established outcome.
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