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

Tahir, Noor Ul Ain; Zhang, Zuping; Asim, Muhammad; Chen, Junhong; ELAffendi, Mohammed

doi:10.3390/a17030103

Cited by 3 publications

(2 citation statements)

References 146 publications

(138 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recognizing these limitations, this work aimed to address the VEVD problem more comprehensively by proposing a single-stage VEVD system that offers fast and accurate predictions. Our main priority was to use state-of-the-art DL-based object detection algorithms for the development of the VEVD system, rather than relying on conventional techniques commonly found in the existing literature [13].…”

Section: Introductionmentioning

confidence: 99%

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

Zohaib,

Asim,

ELAffendi

2024

Mathematics

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Zohaib,

Asim,

ELAffendi

2024

Mathematics

Self Cite

View full text Add to dashboard Cite

show abstract

“…In many critical transportation solutions where drones are used for data acquisition and processing, a range of difficulties arises. Among these, issues concerning images captured by drones are notable, including oblique angles, non-uniform illumination, degradation, blurring, occlusion, and reduced visibility [ 2 ]. Concurrently, the necessity for on-the-fly processing as the drone captures data imposes constraints on the available computational resources [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Fractional B-Spline Wavelets and U-Net Architecture for Robust and Reliable Vehicle Detection in Snowy Conditions

Mokayed,

Ulehla,

Shurdhaj

et al. 2024

Sensors

View full text Add to dashboard Cite

This paper addresses the critical need for advanced real-time vehicle detection methodologies in Vehicle Intelligence Systems (VIS), especially in the context of using Unmanned Aerial Vehicles (UAVs) for data acquisition in severe weather conditions, such as heavy snowfall typical of the Nordic region. Traditional vehicle detection techniques, which often rely on custom-engineered features and deterministic algorithms, fall short in adapting to diverse environmental challenges, leading to a demand for more precise and sophisticated methods. The limitations of current architectures, particularly when deployed in real-time on edge devices with restricted computational capabilities, are highlighted as significant hurdles in the development of efficient vehicle detection systems. To bridge this gap, our research focuses on the formulation of an innovative approach that combines the fractional B-spline wavelet transform with a tailored U-Net architecture, operational on a Raspberry Pi 4. This method aims to enhance vehicle detection and localization by leveraging the unique attributes of the NVD dataset, which comprises drone-captured imagery under the harsh winter conditions of northern Sweden. The dataset, featuring 8450 annotated frames with 26,313 vehicles, serves as the foundation for evaluating the proposed technique. The comparative analysis of the proposed method against state-of-the-art detectors, such as YOLO and Faster RCNN, in both accuracy and efficiency on constrained devices, emphasizes the capability of our method to balance the trade-off between speed and accuracy, thereby broadening its utility across various domains.

show abstract

Enhancing target detection accuracy through cross-modal spatial perception and dual-modality fusion

Zhang,

Zhu

2024

Front. Phys.

View full text Add to dashboard Cite

The disparity between human and machine perception of spatial information presents a challenge for machines to accurately sense their surroundings and improve target detection performance. Cross-modal data fusion emerges as a potential solution to enhance the perceptual capabilities of systems. This article introduces a novel spatial perception method that integrates dual-modality feature fusion and coupled attention mechanisms to validate the improvement in detection performance through cross-modal information fusion. The proposed approach incorporates cross-modal feature extraction through a multi-scale feature extraction structure employing a dual-flow architecture. Additionally, a transformer is integrated for feature fusion, while the information perception of the detection system is optimized through the utilization of a linear combination of loss functions. Experimental results demonstrate the superiority of our algorithm over single-modality target detection using visible images, exhibiting an average accuracy improvement of 30.4%. Furthermore, our algorithm outperforms single-modality infrared image detection by 3.0% and comparative multimodal target detection algorithms by 3.5%. These results validate the effectiveness of our proposed algorithm in fusing dual-band features, significantly enhancing target detection accuracy. The adaptability and robustness of our approach are showcased through these results.

show abstract

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

Cited by 3 publications

References 146 publications

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

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

Fractional B-Spline Wavelets and U-Net Architecture for Robust and Reliable Vehicle Detection in Snowy Conditions

Enhancing target detection accuracy through cross-modal spatial perception and dual-modality fusion

Contact Info

Product

Resources

About