Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions

Liu, Wenyu; Ren, Gexue; Rong, Yu; Guo, Shi; Zhu, Jianke; Zhang, Lei

doi:10.1609/aaai.v36i2.20072

Cited by 244 publications

(94 citation statements)

References 53 publications

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“…The one-stage algorithm is a direct localization regression algorithm, which reduces the number of staging steps than the two-stage algorithm, so the detection speed is faster. Liu et al [47] proposed a distinguishable image processing (DIP) module with an end-to-end approach to jointly learn CNN-PP and YOLOv3 for the task of balancing image enhancement and object detection. Ganesh et al [48] achieved multi-scale feature interaction by exploiting the missing combinatorial connections between various feature scales in the existing state-of-the-art methods, and enhanced both accuracy and detection speed on the improved YOLOv4.…”

Section: Methods Overviewmentioning

confidence: 99%

A Detection Method of Bolts on Axlebox Cover Based on Cascade Deep Convolutional Neural Network

Wang¹,

Li²,

Zheng³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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This paper proposes a cascade deep convolutional neural network to address the loosening detection problem of bolts on axlebox covers. Firstly, an SSD network based on ResNet50 and CBAM module by improving bolt image features is proposed for locating bolts on axlebox covers. And then, the A 2 -PFN is proposed according to the slender features of the marker lines for extracting more accurate marker lines regions of the bolts. Finally, a rectangular approximation method is proposed to regularize the marker line regions as a way to calculate the angle of the marker line and plot all the angle values into an angle table, according to which the criteria of the angle table can determine whether the bolt with the marker line is in danger of loosening. Meanwhile, our improved algorithm is compared with the pre-improved algorithm in the object localization stage. The results show that our proposed method has a significant improvement in both detection accuracy and detection speed, where our mAP (IoU = 0.75) reaches 0.77 and fps reaches 16.6. And in the saliency detection stage, after qualitative comparison and quantitative comparison, our method significantly outperforms other state-of-the-art methods, where our MAE reaches 0.092, F-measure reaches 0.948 and AUC reaches 0.943. Ultimately, according to the angle table, out of 676 bolt samples, a total of 60 bolts are loose, 69 bolts are at risk of loosening, and 547 bolts are tightened.

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Section: Methods Overviewmentioning

confidence: 99%

A Detection Method of Bolts on Axlebox Cover Based on Cascade Deep Convolutional Neural Network

Wang¹,

Li²,

Zheng³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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“…The fourth one uses end-to-end adaptive learning to align detection targets in clear and fogged images for object detection. Liu et al [ 31 ] proposed the IA-YOLO network for object detection in complex environments, where object labels and bounding box coordinates are predicted by a single CNN. This method leads to a high rate of missed detection due to incomplete defogging.…”

Section: Related Workmentioning

confidence: 99%

IDOD-YOLOV7: Image-Dehazing YOLOV7 for Object Detection in Low-Light Foggy Traffic Environments

Qiu

Wang

et al. 2023

Sensors

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Convolutional neural network (CNN)-based autonomous driving object detection algorithms have excellent detection results on conventional datasets, but the detector performance can be severely degraded in low-light foggy weather environments. Existing methods have difficulty in achieving a balance between low-light image enhancement and object detection. To alleviate this problem, this paper proposes a foggy traffic environment object detection framework, IDOD-YOLOV7. This network is based on joint optimal learning of image defogging module IDOD (AOD + SAIP) and YOLOV7 detection modules. Specifically, for low-light foggy images, we propose to improve the image quality by joint optimization of image defogging (AOD) and image enhancement (SAIP), where the parameters of the SAIP module are predicted by a miniature CNN network and the AOD module performs image defogging by optimizing the atmospheric scattering model. The experimental results show that the IDOD module not only improves the image defogging quality for low-light fog images but also achieves better results in objective evaluation indexes such as PSNR and SSIM. The IDOD and YOLOV7 learn jointly in an end-to-end manner so that object detection can be performed while image enhancement is executed in a weakly supervised manner. Finally, a low-light fogged traffic image dataset (FTOD) was built by physical fogging in order to solve the domain transfer problem. The training of IDOD-YOLOV7 network by a real dataset (FTOD) improves the robustness of the model. We performed various experiments to visually and quantitatively compare our method with several state-of-the-art methods to demonstrate its superiority over the others. The IDOD-YOLOV7 algorithm not only suppresses the artifacts of low-light fog images and improves the visual effect of images but also improves the perception of autonomous driving in low-light foggy environments.

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“…Accurate perception of the external environment and obtaining information about the vehicle are of great significance for safe vehicle driving. Image processing and target detection algorithms, as a crucial topic in computer vision, can detect external information accurately and in real-time, which is the primary requirement in the real world of self-driving vehicles [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

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

Yao

Fan

et al. 2022

Sensors

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With the continuous development of artificial intelligence and computer vision technology, autonomous vehicles have developed rapidly. Although self-driving vehicles have achieved good results in normal environments, driving in adverse weather can still pose a challenge to driving safety. To improve the detection ability of self-driving vehicles in harsh environments, we first construct a new color levels offset compensation model to perform adaptive color levels correction on images, which can effectively improve the clarity of targets in adverse weather and facilitate the detection and recognition of targets. Then, we compare several common one-stage target detection algorithms and improve on the best-performing YOLOv5 algorithm. We optimize the parameters of the Backbone of the YOLOv5 algorithm by increasing the number of model parameters and incorporating the Transformer and CBAM into the YOLOv5 algorithm. At the same time, we use the loss function of EIOU to replace the loss function of the original CIOU. Finally, through the ablation experiment comparison, the improved algorithm improves the detection rate of the targets, with the mAP reaching 94.7% and the FPS being 199.86.

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Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions

Cited by 244 publications

References 53 publications

A Detection Method of Bolts on Axlebox Cover Based on Cascade Deep Convolutional Neural Network

A Detection Method of Bolts on Axlebox Cover Based on Cascade Deep Convolutional Neural Network

IDOD-YOLOV7: Image-Dehazing YOLOV7 for Object Detection in Low-Light Foggy Traffic Environments

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

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