Detection of Malaysian Traffic Signs via Modified YOLOv3 Algorithm

Mohd-Isa, Wan-Noorshahida; Abdullah, Md-Shakif; Sarzil, Mahmood; Abdullah, Junaidi; Ali, Aziah; Hashim, Noramiza

doi:10.1109/icdabi51230.2020.9325690

Cited by 11 publications

(5 citation statements)

References 8 publications

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“…It can be clearly seen that our model along with the techniques implemented in this thesis, has outperformed the previous technique in clear condition. The performance of our model even when tested in rainy condition images had higher mAP@0.5 than the result obtained by [14] that was only tested in clear condition.…”

Section: Table 5 Output Comparison Between the Baseline And The Best ...contrasting

confidence: 50%

“…These corresponds to mAP at 50% IoU of 0.8921mAP@0.5 and 0.8340mAP@0.5 respectively. In contrast to this, the model developed by Mohd-Isa, et al (2020), reported in [14], which was trained on the same MTSD dataset only achieved 0.825mAP@0.5 in clear condition. It can be clearly seen that our model along with the techniques implemented in this thesis, has outperformed the previous technique in clear condition.…”

Section: Table 5 Output Comparison Between the Baseline And The Best ...mentioning

confidence: 84%

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Detection and classification of road signs in raining condition with limited dataset

Khan

Shah

Mokhtar

2022

SIViP

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The road sign recognition (RSR) system is used to complete two tasks: localizing the tra c sign in an image and then classifying it according to the image features. Some of the applications of such system are incorporated into the Advanced Driver Assistance System (ADAS) and autonomous vehicles. However, the accuracy of the model decreases when changes in lighting or weather occurs, and the lack of training samples taken in rainy condition causes the model to be sub-optimal. The research was conducted with the focus on solving two problems, the accuracy of an object detection model decreases when changes in lighting or weather occurs and the lack of training data, especially images taken under adverse condition. In this work, we compare and analyze three methods; automatic white balance (AWB), policy augmentations and Image-to-Image-translation (I2IT) technique on their performance to detect tra c signs in raining conditions. All methods were built upon the pre-trained SSD-MobileNetV2 model and using the TensorFlow2 framework. The images from the Malaysia Tra c Sign Dataset (MTSD) are used to train all models. Finally based on the result of the three model a nal combination model is proposed that achieved the best performance in rainy condition. Experimental results showed that AWB was not that effective in detecting road sign in raining condition, while the other two techniques were highly effective. The nal proposed model was implemented by combining policy augmentation and I2IT, obtained an mAP of 0.7967 in clear images and mAP of 0.7160 when rainy images was added to the testing dataset.

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Section: Table 5 Output Comparison Between the Baseline And The Best ...contrasting

confidence: 50%

Section: Table 5 Output Comparison Between the Baseline And The Best ...mentioning

confidence: 84%

Detection and classification of road signs in raining condition with limited dataset

Khan

Shah

Mokhtar

2022

SIViP

View full text Add to dashboard Cite

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“…YOLOv3 model training with traffic signs is used by Mohd-Isa et.al. in Malaysia which resulted in a success of %90 [9]. In the same way, Novak et.al.…”

Section: Introductionmentioning

confidence: 82%

Solution of Real-Time Traffic Signs Detection Problem for Autonomous Vehicles by Using YOLOV4 And Haarcascade Algorithms

Ortatas

Çetin

2023

International Journal of Automotive Science and Technology

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Unmanned systems are increasingly used today to facilitate our daily lives and use time more efficiently. Therefore, this rapidly emerging and growing technology appears in every aspect of our lives with its various functions. Object recognition algorithms are one of the most important functions that we often encounter in these systems. Autonomous vehicle technologies are the latest and fastest-growing technology among unmanned systems. In this study, we investigate the success rates of two different algorithms for recognizing traffic signs and markings that can be used for partially or fully autonomous vehicles. In this study, two different solutions to the problem of recognizing the signs for fully autonomous and fully autonomous vehicles, respectively, were presented and the correct identification of the markers was evaluated. The work was performed in real-time. Two different con-cepts were used for these products. An enclosed space where an ideal lighting environment is provided for the evaluation of models should be visualized. In addition, for the general recognition of the models, the test procedures were performed with a dataset obtained from the users and it was computed for the general recognition. In addition, this study aims to provide a better understanding of the basic working principles, the differences between ma-chine learning and deep learning, and the contents of object recognition processes.

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“…Representative algorithms include You Only Look Once (YOLO [ 21 ]), single shot multibox detector (SSD [ 22 ]), and RetinaNet [ 23 ]. In the field of traffic sign detection, a study [ 24 ] addressed the issue of low accuracy in small object recognition by improving the YOLO network. By integrating spatial pyramid pooling into the YOLOv3 network, it comprehensively learned multi-scale features and effectively enhanced the detection accuracy of traffic signs to 91%.…”

Section: Related Workmentioning

confidence: 99%

A Small Object Detection Algorithm for Traffic Signs Based on Improved YOLOv7

Wang

2023

Sensors

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Traffic sign detection is a crucial task in computer vision, finding wide-ranging applications in intelligent transportation systems, autonomous driving, and traffic safety. However, due to the complexity and variability of traffic environments and the small size of traffic signs, detecting small traffic signs in real-world scenes remains a challenging problem. In order to improve the recognition of road traffic signs, this paper proposes a small object detection algorithm for traffic signs based on the improved YOLOv7. First, the small target detection layer in the neck region was added to augment the detection capability for small traffic sign targets. Simultaneously, the integration of self-attention and convolutional mix modules (ACmix) was applied to the newly added small target detection layer, enabling the capture of additional feature information through the convolutional and self-attention channels within ACmix. Furthermore, the feature extraction capability of the convolution modules was enhanced by replacing the regular convolution modules in the neck layer with omni-dimensional dynamic convolution (ODConv). To further enhance the accuracy of small target detection, the normalized Gaussian Wasserstein distance (NWD) metric was introduced to mitigate the sensitivity to minor positional deviations of small objects. The experimental results on the challenging public dataset TT100K demonstrate that the SANO-YOLOv7 algorithm achieved an 88.7% mAP@0.5, outperforming the baseline model YOLOv7 by 5.3%.

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Detection of Malaysian Traffic Signs via Modified YOLOv3 Algorithm

Cited by 11 publications

References 8 publications

Detection and classification of road signs in raining condition with limited dataset

Detection and classification of road signs in raining condition with limited dataset

Solution of Real-Time Traffic Signs Detection Problem for Autonomous Vehicles by Using YOLOV4 And Haarcascade Algorithms

A Small Object Detection Algorithm for Traffic Signs Based on Improved YOLOv7

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