Real-time vehicle target detection in inclement weather conditions based on YOLOv4

Wang, Rui; Zhao, He; Xu, Zhengwei; Ding, Yaming; Li, Guowei; Zhang, Yuxin; Li, Hua

doi:10.3389/fnbot.2023.1058723

Cited by 9 publications

(3 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another study [138] utilized YOLOv4 with SPP-NET layers for vehicle detection, achieving an 81% mAP. In contrast, the study in [139] focused on harsh weather conditions, introducing YOLOv4 with an anchor-free and decoupled head, albeit achieving a 60.3% mAP and focusing exclusively on a single class. Moreover, the goal of [140] was to enhance self-driving vehicle detection in adverse weather using YOLOv5 with Transformer and CBAM modules, achieving an impressive mAP of 94.7% and FPS of 199.86.…”

Section: Approaches For Vehicle Detectionmentioning

confidence: 99%

Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches

Tahir,

Zhang,

Asim

et al. 2024

Algorithms

View full text Add to dashboard Cite

Enhancing the environmental perception of autonomous vehicles (AVs) in intelligent transportation systems requires computer vision technology to be effective in detecting objects and obstacles, particularly in adverse weather conditions. Adverse weather circumstances present serious difficulties for object-detecting systems, which are essential to contemporary safety procedures, infrastructure for monitoring, and intelligent transportation. AVs primarily depend on image processing algorithms that utilize a wide range of onboard visual sensors for guidance and decisionmaking. Ensuring the consistent identification of critical elements such as vehicles, pedestrians, and road lanes, even in adverse weather, is a paramount objective. This paper not only provides a comprehensive review of the literature on object detection (OD) under adverse weather conditions but also delves into the ever-evolving realm of the architecture of AVs, challenges for automated vehicles in adverse weather, the basic structure of OD, and explores the landscape of traditional and deep learning (DL) approaches for OD within the realm of AVs. These approaches are essential for advancing the capabilities of AVs in recognizing and responding to objects in their surroundings. This paper further investigates previous research that has employed both traditional and DL methodologies for the detection of vehicles, pedestrians, and road lanes, effectively linking these approaches with the evolving field of AVs. Moreover, this paper offers an in-depth analysis of the datasets commonly employed in AV research, with a specific focus on the detection of key elements in various environmental conditions, and then summarizes the evaluation matrix. We expect that this review paper will help scholars to gain a better understanding of this area of research.

show abstract

Section: Approaches For Vehicle Detectionmentioning

confidence: 99%

Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches

Tahir,

Zhang,

Asim

et al. 2024

Algorithms

View full text Add to dashboard Cite

show abstract

“…Li et al [32] designed a Res-YOLO network model, which significantly reduces the leakage rate and improves the vehicle target detection accuracy. Wang et al [33] provided a YOLOv4 enhancement method to solve the problem of difficult vehicle target detection in bad weather. CAO et al [34] proposed a multi-scale target detection network that significantly improves the detection accuracy of small-size targets.…”

Section: Reated Work a Object Detection In Autonomous Driving Scenariosmentioning

confidence: 99%

DCW-YOLO: Road Object Detection Algorithms for Autonomous Driving

Ren,

Jing,

2024

IEEE Access

View full text Add to dashboard Cite

Aiming at the problems of multiple parameters and poor detection accuracy of object detection network in automatic driving scenarios, an object detection algorithm based on improved YOLOv8 is proposed. First, a dynamic head framework is used to unify the object detection head and the attention mechanism, and the attention mechanism is used for scale-awareness, spatial-awareness, and task-awareness, respectively, which significantly improves the representation capability of the object detection head without increasing the computational overhead. Second, the Coordinate Attention mechanism is embedded in the SPPF layer, which embeds the target's location information into the channel attention to offer more precise localization for the model, suppress irrelevant aspects, and enable greater integration of local and global characteristics. Finally, the deleterious gradients generated by low-quality examples are reduced using the Wise-IoU v3 bounding box loss function in conjunction with a dynamic non-monotonic focusing mechanism utilizing an anchor box gradient gain assignment strategy. On the challenging public dataset KITTI, the accuracy is improved by 2.1% compared to the benchmark algorithm. In addition, the excellent performance on CCTSDB2021 and VOC highlights the generalization performance of the improved model.

show abstract

“…Despite applying augmentation techniques, only two types of augmentations (hue and saturation) were added in sandy weather. In [28], the authors also aimed to detect vehicles in severe weather. YOLOv4 was proposed, with an anchor-free and decoupled head.…”

Section: Related Workmentioning

confidence: 99%

Object Detection Performance Evaluation for Autonomous Vehicles in Sandy Weather Environments

Aloufi,

Alnori,

Thayananthan

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

In order to reach the highest level of automation, autonomous vehicles (AVs) are required to be aware of surrounding objects and detect them even in adverse weather. Detecting objects is very challenging in sandy weather due to characteristics of the environment, such as low visibility, occlusion, and changes in lighting. In this paper, we considered the You Only Look Once (YOLO) version 5 and version 7 architectures to evaluate the performance of different activation functions in sandy weather. In our experiments, we targeted three activation functions: Sigmoid Linear Unit (SiLU), Rectified Linear Unit (ReLU), and Leaky Rectified Linear Unit (LeakyReLU). The metrics used to evaluate their performance were precision, recall, and mean average precision (mAP). We used the Detection in Adverse Weather Nature (DAWN) dataset which contains various weather conditions, though we selected sandy images only. Moreover, we extended the DAWN dataset and created an augmented version of the dataset using several augmentation techniques, such as blur, saturation, brightness, darkness, noise, exposer, hue, and grayscale. Our results show that in the original DAWN dataset, YOLOv5 with the LeakyReLU activation function surpassed other architectures with respect to the reported research results in sandy weather and achieved 88% mAP. For the augmented DAWN dataset that we developed, YOLOv7 with SiLU achieved 94% mAP.

show abstract

Real-time vehicle target detection in inclement weather conditions based on YOLOv4

Cited by 9 publications

References 65 publications

Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches

Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches

DCW-YOLO: Road Object Detection Algorithms for Autonomous Driving

Object Detection Performance Evaluation for Autonomous Vehicles in Sandy Weather Environments

Contact Info

Product

Resources

About