Adverse Weather Target Detection Algorithm Based on Adaptive Color Levels and Improved YOLOv5

Yao, Jiale; Fan, Xiaolong; Li, Bing; Qin, Wenlin

doi:10.3390/s22218577

Cited by 20 publications

(11 citation statements)

References 38 publications

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“…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. The DL approach proposed in [141] for nighttime vehicle detection in autonomous cars, combining a Generative Adversarial Network for image translation and YOLOv5 for detection, achieved a high accuracy of 96.75%, significantly enhancing the reliability of AV recognition models for night conditions.…”

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

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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.

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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

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show abstract

“…[35], [36] added detection heads for small targets to capture multi-scale information, significantly improving the model's performance in detecting small targets. [37], [38] improved the model's small object detection performance by optimizing the loss function. Data augment (e.g., geometric transformations, color transformations, random occlusion, etc.)…”

Section: B Small Object Detectionmentioning

confidence: 99%

YOLOv8n_BT: Research on Classroom Learning Behavior Recognition Algorithm Based on Improved YOLOv8n

Liu,

Jiang,

et al. 2024

IEEE Access

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Classroom learning behavior recognition can provide effective technical support for teaching and learning. However, in natural classroom teaching scenarios, classroom learning behaviors are often missed or falsely detected due to character occlusion and the small object. To tackle the above issues, this study proposed an improved classroom learning behavior recognition algorithm (YOLOv8n_BT) based on YOLOv8n. On the one hand, for the occlusion problem of classroom learning behaviors, this study incorporated the BRA into the Backbone to better capture feature information; on the other hand, for the small object problem of classroom learning behaviors for back-row-students, this study expanded a Tiny Object Detection Layer (TODL) to detect small targets better. Experiments show that the BRA and the TODL can significantly improve the model performance. The YOLOv8n_BT model, which incorporated both the BRA and the TODL into the YOLOv8n(baseline) model simultaneously, has the most significant performance improvement. Compared with the YOLOv8n(baseline), the YOLOv8n_BT model improved by 3.0%, 6.7%, 5.0%, 3.6%, and 9.0% on P, R, F1, mAP50, and mAP50-90, respectively. The detection performance of YOLOv8n_BT also outperforms other state-of-the-arts.

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“…Many researchers have focused on the attention mechanism, aiming to improve the backbone network by adding additional attention mechanisms to focus on relevant features and enhance the detection performance. For example, Luo X et al [ 25 ] added an IECA attention module after the Focus module, Yao J et al [ 26 ] introduced spatial channel mixed attention mechanism to the backbone network, and Qiu S et al [ 27 ] incorporated coordinate attention mechanism during feature extraction. These researchers have demonstrated through numerous experiments that attention mechanisms can help the network focus on relevant features and enhance feature extraction capabilities.…”

Section: Related Workmentioning

confidence: 99%

YOLOv5s-DSD: An Improved Aerial Image Detection Algorithm Based on YOLOv5s

Sun

Chen

Xiao

et al. 2023

Sensors

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Due to the challenges of small detection targets, dense target distribution, and complex backgrounds in aerial images, existing object detection algorithms perform poorly in aerial image detection tasks. To address these issues, this paper proposes an improved algorithm called YOLOv5s-DSD based on YOLOv5s. Specifically, the SPDA-C3 structure is proposed and used to reduce information loss while focusing on useful features, effectively tackling the challenges of small detection targets and complex backgrounds. The novel decoupled head structure, Res-DHead, is introduced, along with an additional small object detection head, further improving the network’s performance in detecting small objects. The original NMS is replaced by Soft-NMS-CIOU to address the issue of neighboring box suppression caused by dense object distribution. Finally, extensive ablation experiments and comparative tests are conducted on the VisDrone2019 dataset, and the results demonstrate that YOLOv5s-DSD outperforms current state-of-the-art object detection models in aerial image detection tasks. The proposed improved algorithm achieves a significant improvement compared with the original algorithm, with an increase of 17.4% in mAP@0.5 and 16.4% in mAP@0.5:0.95, validating the superiority of the proposed improvements.

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Adverse Weather Target Detection Algorithm Based on Adaptive Color Levels and Improved YOLOv5

Cited by 20 publications

References 38 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

YOLOv8n_BT: Research on Classroom Learning Behavior Recognition Algorithm Based on Improved YOLOv8n

YOLOv5s-DSD: An Improved Aerial Image Detection Algorithm Based on YOLOv5s

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