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1. Towards safer roads: benchmarking object detection models in complex weather scenarios

   https://doi.org/10.1007/s00138-025-01709-5  

   Tran-Le, Ba-Thinh; Patel, Vatsa; Huynh, Viet-Tham; Tran, Mai-Khiem; Agrawal, Kunal; Tran, Minh-Triet; Nguyen, Tam V (July 2025, Machine Vision and Applications)

   The performance of object detection models in adverse weather conditions remains a critical challenge for intelligent transportation systems. Since advancements in autonomous driving rely heavily on extensive datasets, which help autonomous driving systems be reliable in complex driving environments, this study provides a comprehensive dataset under diverse weather scenarios like rain, haze, nighttime, or sun flares and systematically evaluates the robustness of state-of-the-art deep learning-based object detection frameworks. Our Adverse Driving Conditions Dataset features eight single weather effects and four challenging mixed weather effects, with a curated collection of 50,000 traffic images for each weather effect. State-of-the-art object detection models are evaluated using standard metrics, including precision, recall, and IoU. Our findings reveal significant performance degradation under adverse conditions compared to clear weather, highlighting common issues such as misclassification and false positives. For example, scenarios like haze combined with rain cause frequent detection failures, highlighting the limitations of current algorithms. Through comprehensive performance analysis, we provide critical insights into model vulnerabilities and propose directions for developing weather-resilient object detection systems. This work contributes to advancing robust computer vision technologies for safer and more reliable transportation in unpredictable real-world environments. 

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2. Deblending and Classifying Astronomical Sources with Mask R-CNN Deep Learning

   Colin J Burke, Patrick D (October 2019, Monthly notices of the Royal Astronomical Society)

   e apply a new deep learning technique to detect, classify, and deblend sources in multi-band astronomical images. We train and evaluate the performance of an artificial neural network built on the Mask R-CNN image processing framework, a general code for efficient object detection, classification, and instance segmentation. After evaluating the performance of our network against simulated ground truth images for star and galaxy classes, we find a precision of 92% at 80% recall for stars and a precision of 98% at 80% recall for galaxies in a typical field with ∼30 galaxies/arcmin2. We investigate the deblending capability of our code, and find that clean deblends are handled robustly during object masking, even for significantly blended sources. This technique, or extensions using similar network architectures, may be applied to current and future deep imaging surveys such as LSST and WFIRST. Our code, Astro R-CNN, is publicly available at https://github.com/burke86/astro_rcnn 

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3. Mapping Electric Transmission Line Infrastructure from Aerial Imagery with Deep Learning

   https://doi.org/10.1109/IGARSS39084.2020.9323851  

   Hu, Wei; Alexander, Ben; Cathcart, Wendell; Hu, Atsushi; Nair, Varun; Zuo, Lin; Malof, Jordan; Collins, Leslie; Bradbury, Kyle (September 2020, IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium)

   null (Ed.)

   Access to electricity positively correlates with many beneficial socioeconomic outcomes in the developing world including improvements in education, health, and poverty. Efficient planning for electricity access requires information on the location of existing electric transmission and distribution infrastructure; however, the data on existing infrastructure is often unavailable or expensive. We propose a deep learning based method to automatically detect electric transmission infrastructure from aerial imagery and quantify those results with traditional object detection performance metrics. In addition, we explore two challenges to applying these techniques at scale: (1) how models trained on particular geographies generalize to other locations and (2) how the spatial resolution of imagery impacts infrastructure detection accuracy. Our approach results in object detection performance with an F1 score of 0.53 (0.47 precision and 0.60 recall). Using training data that includes more diverse geographies improves performance across the 4 geographies that we examined. Image resolution significantly impacts object detection performance and decreases precipitously as the image resolution decreases. 

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4. Cyber Mobility Mirror: A Deep Learning-Based Real-World Object Perception Platform Using Roadside LiDAR

   https://doi.org/10.1109/TITS.2023.3268281  

   Bai, Zhengwei; Nayak, Saswat P; Zhao, Xuanpeng; Wu, Guoyuan; Barth, Matthew J; Qi, Xuewei; Liu, Yongkang; Sisbot, Emrah Akin; Oguchi, Kentaro (September 2023, IEEE Transactions on Intelligent Transportation Systems)

   Abstract—Object perception plays a fundamental role in Cooperative Driving Automation (CDA) which is regarded as a revolutionary promoter for next-generation transportation systems. However, the vehicle-based perception may suffer from the limited sensing range and occlusion as well as low penetration rates in connectivity. In this paper, we propose Cyber Mobility Mirror (CMM), a next-generation real-world object perception system for 3D object detection, tracking, localization, and reconstruction, to explore the potential of roadside sensors for enabling CDA in the real world. The CMM system consists of six main components: i) the data pre-processor to retrieve and preprocess the raw data; ii) the roadside 3D object detector to generate 3D detection results; iii) the multi-object tracker to identify detected objects; iv) the global locator to generate geo-localization information; v) the mobile-edge-cloud-based communicator to transmit perception information to equipped vehicles, and vi) the onboard advisor to reconstruct and display the real-time traffic conditions. An automatic perception evaluation approach is proposed to support the assessment of data-driven models without human-labeling requirements and a CMM field-operational system is deployed at a real-world intersection to assess the performance of the CMM. Results from field tests demonstrate that our CMM prototype system can achieve 96.99% precision and 83.62% recall for detection and 73.55% ID-recall for tracking. High-fidelity real-time traffic conditions (at the object level) can be geo-localized with a root-mean-square error (RMSE) of 0.69m and 0.33m for lateral and longitudinal direction, respectively, and displayed on the GUI of the equipped vehicle with a frequency of 3 − 4Hz. 

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5. Application of image processing and convolutional neural networks for flood image classification and semantic segmentation

   https://doi.org/10.1016/j.envsoft.2021.105285  

   Pall, R. and (January 2022, Environmental modelling software)

   Deep learning algorithms are exceptionally valuable tools for collecting and analyzing the catastrophic readiness and countless actionable flood data. Convolutional neural networks (CNNs) are one form of deep learning algorithms widely used in computer vision which can be used to study flood images and assign learnable weights to various objects in the image. Here, we leveraged and discussed how connected vision systems can be used to embed cameras, image processing, CNNs, and data connectivity capabilities for flood label detection. We built a training database service of >9000 images (image annotation service) including the image geolocation information by streaming relevant images from social media platforms, Department of Transportation (DOT) 511 traffic cameras, the US Geological Survey (USGS) live river cameras, and images downloaded from search engines. We then developed a new python package called “FloodImageClassifier” to classify and detect objects within the collected flood images. “FloodImageClassifier” includes various CNNs architectures such as YOLOv3 (You look only once version 3), Fast R–CNN (Region-based CNN), Mask R–CNN, SSD MobileNet (Single Shot MultiBox Detector MobileNet), and EfficientDet (Efficient Object Detection) to perform both object detection and segmentation simultaneously. Canny Edge Detection and aspect ratio concepts are also included in the package for flood water level estimation and classification. The pipeline is smartly designed to train a large number of images and calculate flood water levels and inundation areas which can be used to identify flood depth, severity, and risk. “FloodImageClassifier” can be embedded with the USGS live river cameras and 511 traffic cameras to monitor river and road flooding conditions and provide early intelligence to emergency response authorities in real-time. 

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