Enhanced Method of Detecting Wearing of Helmets in Traffic Using HOG-Sobel and Decision Tree Method
Keywords:
Helmet Detection, Decision Tree, HOG-Sobel, Classification, Machine LearningAbstract
The disregard for road safety by those operating two-wheeled vehicles frequently leads to accidents and fatalities. Numerous nations have implemented a compulsory requirement for individuals to wear helmets when operating two-wheeled vehicles. If the riders do not wear helmets, they face an accident. This will lead to dangerous head or brain injury due to riding without protection. For this reason, we constructed a model based on HoG-Sobel fusion approach and a decision tree method to identify precisely the presence of a helmet on a person depicted in a picture. In this work, the author engages in the process of capturing photographs and subsequently extracting their attributes through the utilization of image processing techniques. Next, a model is constructed via machine learning techniques based on the aforementioned retrieved attributes. In this analysis, a comparison is conducted on several image processing and classification algorithms utilized for the given dataset. To enforce this requirement, a system has been constructed utilizing Tensorflow and Keras within the domain of computer vision. The decision tree classifier achieved the highest accuracy as compared to various classification models that were examined. This technique appears to be effective to ascertain whether individuals operating two-wheeled vehicles use a helmet for head protection. The use of helmets, designed to preserve human life will be impacted.
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R. Waranusast, N. Bundon, V. Tangnoi, and P. Pattanathaburt, “ Machine vision techniques for motorcycle safety helmet detection,” In 28th International conference on Image and Vision Computing New Zealand(IVCNZ), IEEE, pp. 35–40, Nov 2013.
R. R. V. e Silva, K. R. T. Aires, and R. D. M. S. Veras, “Helmet detection on motorcyclists using image descriptors and classifiers,” In 27th SIBGRAPI Conference on graphics, patterns and images, IEEE, pp. 141–148, 2014.
J. Li, H. Liu, T. Wang, M. Jiang, S. Wang, K. Li, and X. Zhao, “Safety helmet wearing detection based on image processing and machine learning,,” In 9th International Conference on Advanced Computational Intelligence (ICACI) IEEE, pp. 201–205, 2017.
M. M. Raut, M. D. Shende, M. P. Meshram, M. B. Nimgade, and A. Ghasad “Helmet detection on two-wheeler riders using machine learning,” International Journal for Research in Applied Science & Engineering Technology (IJRASET), vol. 11(IV), pp. 1450–1453, [Online] Available: Apr, 2023.
A. Soni, and A. P. Singh, “Automatic motorcyclist helmet rule violation detection using tensorflow & keras in opencv,” In IEEE International Students’ Conference on Electrical, Electronics and Computer Science(SCEECS), IEEE, pp. 1-5, 2020.
N. H. Febriana, “Analysis of helmet detection on motor drivers to detect traffic violations using the you only look once method (yolov4),” Junal Multidisiplin Madani, vol. 3(7), pp. 1451-1460, 2023.
G. Premmaran, and P. Sathishkumar, “Detection of helmetless riders and automatic number plate recognition using machine learning,” In International Conference on Applied Artificial Intelligence and Computing (ICAAIC), IEEE, pp. 339-345, 2022.
N. D. T. Yung, W. Wong, F. H. Juwono, and Z. A. Sim, “Safety helmet detection using deep learning: Implementation and comparative study using yolov5, yolov6, and yolov7,” In International Conference on Green Energy, Computing and Sustainable Technology (GECOST), IEEE, pp. 164–170, 2022.
J. Wu, N. Cai, W. Chen, H. Wang, and G. Wang, “Automatic detection of hard-hats worn by construction personnel: A deep learning approach and benchmark dataset,” Automation in Construction 106, 102894, 2019.
A. Jangra, Face Mask Dataset. [Online]. Available: https://www.kaggle.com/datasets/ashishjangra27/face-mask-dataset.
Jessica Li, without face mask images: CelebFace dataset. [Online]. Available: https://www.kaggle.com/jesscicali9530/celebface-dataset.
M. R. Kagale, and P. M. Kamble, ”Wood images classification based on various types of k-nn classifier,” In International Conference on Communications and Cyber Physical Engineering, Singapore: Springer Nature Singapore, pp. 775-781, March 2023.
P. K. Srivastava, G. Singh, S. Kumar, N. K. Jain, and V. Bali, “Gabor filter and centre symmetric-local binary pattern based technique for forgery detection in images,” Multimedia Tools and Applications, vol. 4, pp. 1-39, Nov 2023.
S. Shivashankar, and M. R. Kagale, “Combined intensity and color channel information for content based image retrieval,” International Journal of Scientific Research in Computer Science Applications and Management Studies (IJSRCSAMS),vol. 5, no. 7, pp. 1-6, 2018.
H. Lin, J. D. Deng, D. Albers, and F. W. Siebert, “Helmet use detection of tracked motorcycles using cnn-based multi-task learning,” IEEE Access, 8, pp. 162073-162084, 2020.
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