Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey

Berwo, Michael Abebe; Khan, Asad; Fang, Yong; Fahim, Hamza; Javaid, Shumaila; Mahmood, Jabar; Abideen, Zain Ul; M.S., Syam

doi:10.3390/s23104832

Cited by 15 publications

(5 citation statements)

References 135 publications

(194 reference statements)

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“…While there has been extensive research on object detection in on-road scenarios, and thus the corresponding public datasets, particularly in the context of autonomous driving applications [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25], there is a noticeable scarcity of studies addressing object detection in off-road environments due to data limitations. For instance, a study by [26] evaluates a person detection algorithm in off-road environments, considering occlusion and non-standard poses.…”

Section: Object Detection In Off-road Environmentmentioning

confidence: 99%

Automated Region of Interest-Based Data Augmentation for Fallen Person Detection in Off-Road Autonomous Agricultural Vehicles

Baek,

Yu,

Son

et al. 2024

Sensors

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Due to the global population increase and the recovery of agricultural demand after the COVID-19 pandemic, the importance of agricultural automation and autonomous agricultural vehicles is growing. Fallen person detection is critical to preventing fatal accidents during autonomous agricultural vehicle operations. However, there is a challenge due to the relatively limited dataset for fallen persons in off-road environments compared to on-road pedestrian datasets. To enhance the generalization performance of fallen person detection off-road using object detection technology, data augmentation is necessary. This paper proposes a data augmentation technique called Automated Region of Interest Copy-Paste (ARCP) to address the issue of data scarcity. The technique involves copying real fallen person objects obtained from public source datasets and then pasting the objects onto a background off-road dataset. Segmentation annotations for these objects are generated using YOLOv8x-seg and Grounded-Segment-Anything, respectively. The proposed algorithm is then applied to automatically produce augmented data based on the generated segmentation annotations. The technique encompasses segmentation annotation generation, Intersection over Union-based segment setting, and Region of Interest configuration. When the ARCP technique is applied, significant improvements in detection accuracy are observed for two state-of-the-art object detectors: anchor-based YOLOv7x and anchor-free YOLOv8x, showing an increase of 17.8% (from 77.8% to 95.6%) and 12.4% (from 83.8% to 96.2%), respectively. This suggests high applicability for addressing the challenges of limited datasets in off-road environments and is expected to have a significant impact on the advancement of object detection technology in the agricultural industry.

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Section: Object Detection In Off-road Environmentmentioning

confidence: 99%

Automated Region of Interest-Based Data Augmentation for Fallen Person Detection in Off-Road Autonomous Agricultural Vehicles

Baek,

Yu,

Son

et al. 2024

Sensors

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“…Additionally, we introduce the weight factor λ noobj to regulate the impact of the loss in grid cells lacking objects. The specific formula for objectness loss can be found in Equation (9).…”

Section: Detection Of Emergency Vehicles Using Visual Cuesmentioning

confidence: 99%

“…A review conducted in [8] focused on different object detection algorithms and highlighted the suitability of YOLO [5] for vision-based EVD due to its fast processing speed. In addition, the survey paper in [9] provided a comprehensive study on how deep learning (DL) methods are powerful for vehicle detection. By comparing DL methods with traditional techniques, the authors stated that DL models provide efficiency by automating the process of feature extraction, especially since vehicle detection datasets are huge.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Emergency Vehicle Detection: A Deep Learning Approach with Multimodal Fusion

Zohaib,

Asim,

ELAffendi

2024

Mathematics

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Emergency vehicle detection plays a critical role in ensuring timely responses and reducing accidents in modern urban environments. However, traditional methods that rely solely on visual cues face challenges, particularly in adverse conditions. The objective of this research is to enhance emergency vehicle detection by leveraging the synergies between acoustic and visual information. By incorporating advanced deep learning techniques for both acoustic and visual data, our aim is to significantly improve the accuracy and response times. To achieve this goal, we developed an attention-based temporal spectrum network (ATSN) with an attention mechanism specifically designed for ambulance siren sound detection. In parallel, we enhanced visual detection tasks by implementing a Multi-Level Spatial Fusion YOLO (MLSF-YOLO) architecture. To combine the acoustic and visual information effectively, we employed a stacking ensemble learning technique, creating a robust framework for emergency vehicle detection. This approach capitalizes on the strengths of both modalities, allowing for a comprehensive analysis that surpasses existing methods. Through our research, we achieved remarkable results, including a misdetection rate of only 3.81% and an accuracy of 96.19% when applied to visual data containing emergency vehicles. These findings represent significant progress in real-world applications, demonstrating the effectiveness of our approach in improving emergency vehicle detection systems.

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“…But, by first detecting the objects with DL models, and then proceeding to match features via the conventional tracking methodologies, better performance was attained. Regarding [38]'s methods for tracking via detection, we employ a tracking technique called Deep SORT in conjunction with low-confidence track filtering. As a result, fewer false positives were generated using the baseline Deep SORT algorithm.…”

Section: Vehicle Trackingmentioning

confidence: 99%

Automated Recognition and Tracking of Stationary and Moving Cars in Images and Videos: YOLOv5 and SSD Analysis

Priyanka Ankireddy,

S. Gopalakrishnan S.,

V. Lokeswara Reddy

2024

ARASET

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The detection and tracking of cars are a significant and useful aspect of traffic surveillance systems, which is essential for the efficient management of traffic and the security of drivers and passengers. The primary objectives of this investigation are vehicle detection and tracking. The automatic detection of cars in both still photos and video recordings are the main topic of this investigation. One of the many uses for Deep Learning, which combines fuzzy logic, neural networks, and evolutionary algorithms, is the detection and tracking of moving objects. In this work, the key object detection techniques YOLOv5 and SSD were used to analyse vehicle recognition and tracking with deep learning. The Single Shot Multi-Box Detector model architecture is then utilized as the main foundation for the detection of vehicles. The vehicle recognition model is then trained using the YOLOv5 and SSD algorithms, each of which contributes to the illustration of the detection effect. Comparing the detection rates obtained by both models on a range of cars is necessary to locate it. The objective of this work is to create an automated system for locating and tracking both moving and stationary cars in still images and moving movies. According to the research, using this method has improved the success rate of recognizing automobiles to 97.65%.

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Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey

Cited by 15 publications

References 135 publications

Automated Region of Interest-Based Data Augmentation for Fallen Person Detection in Off-Road Autonomous Agricultural Vehicles

Automated Region of Interest-Based Data Augmentation for Fallen Person Detection in Off-Road Autonomous Agricultural Vehicles

Enhancing Emergency Vehicle Detection: A Deep Learning Approach with Multimodal Fusion

Automated Recognition and Tracking of Stationary and Moving Cars in Images and Videos: YOLOv5 and SSD Analysis

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