A Feature Fusion Method to Improve the Driving Obstacle Detection Under Foggy Weather

He, Yongjiang; Liu, Zhaohui

doi:10.1109/tte.2021.3080690

Cited by 30 publications

(11 citation statements)

References 29 publications

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“…The model enhances object detection performance in hazy conditions compared to the base detection network. He and Liu 37 proposed a feature fusion method, which already fuses object features in hazy weather during the training of network parameters. Consequently, hazy images can be used directly for object detection without requiring defogging processing, thereby saving image processing time.…”

Section: Related Workmentioning

confidence: 99%

Small object detection for autonomous driving under hazy conditions on mountain motorways

Wang,

Qiu,

Jiang

et al. 2023

Opt. Eng.

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To address the issue of high miss rates for distant small objects and the diminished system detection performance due to the influence of hazy when autonomous vehicles operate on mountain highways. We propose a framework for small object vehicle detection in hazy traffic environments (SHTDet). This framework aims to enhance small object detection for autonomous driving under hazy conditions on mountainous motorways. Specifically, to restore the clarity of hazy images, we designed an image enhancement (IE), and its parameters are predicted by a convolutional neural network [filter parameter estimation (FPE)]. In addition, to enhance the detection accuracy of small objects, we introduce a cascaded sparse query (CSQ) mechanism, which effectively utilizes high-resolution features while maintaining fast detection speed. We jointly optimize the IE and the detection network (CSQ-FCOS) in an end-to-end manner, ensuring that FPE module can learn a suitable IE. Our proposed SHTDet method is adept at adaptively handling sunny and hazy conditions. Extensive experiments demonstrate the efficacy of the SHTDet method in detecting small objects on hazy sections of mountain highways.

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Section: Related Workmentioning

confidence: 99%

Small object detection for autonomous driving under hazy conditions on mountain motorways

Wang,

Qiu,

Jiang

et al. 2023

Opt. Eng.

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“…The object detection performance of Faster R-CNN [122] in four levels of foggy weather: clear (no fog), light, medium, heavy is analysed in [159]. Other works could be found in [160]- [162].…”

Section: ) Self-driving Scenariosmentioning

confidence: 99%

Toward Performing Image Classification and Object Detection With Convolutional Neural Networks in Autonomous Driving Systems: A Survey

Turay

Vladimirova

2022

IEEE Access

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“…Target detection based on deep learning has demonstrated unique advantages in autonomous driving. This approach is crucial for autonomous driving systems because it can achieve high detection precision with fewer computational resources (He and Liu, 2021 ). The most frequent frameworks for target identification fall into two categories: R-CNN (van de Sande et al, 2011 ), Fast R-CNN (Girshick, 2015 ), and Faster R-CNN (Ren et al, 2017 ) and two-stage target detection algorithms.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Zhao

et al. 2023

Front. Neurorobot.

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As a crucial component of the autonomous driving task, the vehicle target detection algorithm directly impacts driving safety, particularly in inclement weather situations, where the detection precision and speed are significantly decreased. This paper investigated the You Only Look Once (YOLO) algorithm and proposed an enhanced YOLOv4 for real-time target detection in inclement weather conditions. The algorithm uses the Anchor-free approach to tackle the problem of YOLO preset anchor frame and poor fit. It better adapts to the detected target size, making it suitable for multi-scale target identification. The improved FPN network transmits feature maps to unanchored frames to expand the model's sensory field and maximize the utilization of model feature data. Decoupled head detecting head to increase the precision of target category and location prediction. The experimental dataset BDD-IW was created by extracting specific labeled photos from the BDD100K dataset and fogging some of them to test the proposed method's practical implications in terms of detection precision and speed in Inclement weather conditions. The proposed method is compared to advanced target detection algorithms in this dataset. Experimental results indicated that the proposed method achieved a mean average precision of 60.3%, which is 5.8 percentage points higher than the original YOLOv4; the inference speed of the algorithm is enhanced by 4.5 fps compared to the original, reaching a real-time detection speed of 69.44 fps. The robustness test results indicated that the proposed model has considerably improved the capacity to recognize targets in inclement weather conditions and has achieved high precision in real-time detection.

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A Feature Fusion Method to Improve the Driving Obstacle Detection Under Foggy Weather

Cited by 30 publications

References 29 publications

Small object detection for autonomous driving under hazy conditions on mountain motorways

Small object detection for autonomous driving under hazy conditions on mountain motorways

Toward Performing Image Classification and Object Detection With Convolutional Neural Networks in Autonomous Driving Systems: A Survey

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

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