Vehicle Detection And Accident Prediction In Sand/Dust Storms

Singh, Arun; Kumar, Dora Praveen; Shivaprasad, Kelothu; Mohit, Mohit; Wadhawan, Ankita

doi:10.1109/iccs54944.2021.00029

Cited by 22 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Experimental results indicate that the proposed approach is both robust and accurate, especially for complex scenarios. Similarly, the study [ 25 ] proposes an approach for vehicle detection in sandstorms using videos collected from YouTube for sandstorms in different countries including Kuwait, Saudi Arabia, Arizona, etc. The study also utilized the Traffic-Net dataset which contains videos of burning vehicles due to accidents and other complex situations.…”

Section: Related Workmentioning

confidence: 99%

Analyzing Performance of YOLOx for Detecting Vehicles in Bad Weather Conditions

Ashraf,

Hur,

Kim

et al. 2024

Sensors

View full text Add to dashboard Cite

Recent advancements in computer vision technology, developments in sensors and sensor-collecting approaches, and the use of deep and transfer learning approaches have excelled in the development of autonomous vehicles. On-road vehicle detection has become a task of significant importance, especially due to exponentially increasing research on autonomous vehicles during the past few years. With high-end computing resources, a large number of deep learning models have been trained and tested for on-road vehicle detection recently. Vehicle detection may become a challenging process especially due to varying light and weather conditions like night, snow, sand, rain, foggy conditions, etc. In addition, vehicle detection should be fast enough to work in real time. This study investigates the use of the recent YOLO version, YOLOx, to detect vehicles in bad weather conditions including rain, fog, snow, and sandstorms. The model is tested on the publicly available benchmark dataset DAWN containing images containing four bad weather conditions, different illuminations, background, and number of vehicles in a frame. The efficacy of the model is evaluated in terms of precision, recall, and mAP. The results exhibit the better performance of YOLOx-s over YOLOx-m and YOLOx-l variants. YOLOx-s has 0.8983 and 0.8656 mAP for snow and sandstorms, respectively, while its mAP for rain and fog is 0.9509 and 0.9524, respectively. The performance of models is better for snow and foggy weather than rainy weather sandstorms. Further experiments indicate that enhancing image quality using multiscale retinex improves YOLOx performance.

show abstract

Section: Related Workmentioning

confidence: 99%

Analyzing Performance of YOLOx for Detecting Vehicles in Bad Weather Conditions

Ashraf,

Hur,

Kim

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…After AE, fast RCNN and YOLO v3 was used for training and results comparison. Some other works where filters are applied as preprocessing the images before they are passed to deep learning model include dehazing, masking and thresholding [114], (automatic white balance combined with Laplacian pyramids) AWBLP [115,116]. Motivated by the idea of saving processing through transfer learning [117] applied frozen weights of SqueezNet [118], ResNet50 [93] and EfficientNet [14] to train on DAWN dataset to detect vehicles under six different weather conditions.…”

mentioning

confidence: 99%

Traffic Management: Multi-Scale Vehicle Detection in Varying Weather Conditions Using YOLOv4 and Spatial Pyramid Pooling Network

et al. 2022

View full text Add to dashboard Cite

Detecting and counting on road vehicles is a key task in intelligent transport management and surveillance systems. The applicability lies both in urban and highway traffic monitoring and control, particularly in difficult weather and traffic conditions. In the past, the task has been performed through data acquired from sensors and conventional image processing toolbox. However, with the advent of emerging deep learning based smart computer vision systems the task has become computationally efficient and reliable. The data acquired from road mounted surveillance cameras can be used to train models which can detect and track on road vehicles for smart traffic analysis and handling problems such as traffic congestion particularly in harsh weather conditions where there are poor visibility issues because of low illumination and blurring. Different vehicle detection algorithms focusing the same issue deal only with on or two specific conditions. In this research, we address detecting vehicles in a scene in multiple weather scenarios including haze, dust and sandstorms, snowy and rainy weather both in day and nighttime. The proposed architecture uses CSPDarknet53 as baseline architecture modified with spatial pyramid pooling (SPP-NET) layer and reduced Batch Normalization layers. We also augment the DAWN Dataset with different techniques including Hue, Saturation, Exposure, Brightness, Darkness, Blur and Noise. This not only increases the size of the dataset but also make the detection more challenging. The model obtained mean average precision of 81% during training and detected smallest vehicle present in the image

show abstract

A hybrid framework for heterogeneous object detection amidst diverse and adverse weather conditions employing Enhanced-DARTS

Kumar,

Gautam

2024

Int. j. inf. tecnol.

View full text Add to dashboard Cite

Vehicle Detection And Accident Prediction In Sand/Dust Storms

Cited by 22 publications

References 15 publications

Analyzing Performance of YOLOx for Detecting Vehicles in Bad Weather Conditions

Analyzing Performance of YOLOx for Detecting Vehicles in Bad Weather Conditions

Traffic Management: Multi-Scale Vehicle Detection in Varying Weather Conditions Using YOLOv4 and Spatial Pyramid Pooling Network

A hybrid framework for heterogeneous object detection amidst diverse and adverse weather conditions employing Enhanced-DARTS

Contact Info

Product

Resources

About