Helmet Detection Based on Deep Learning and Random Forest on UAV for Power Construction Safety

• Published in: Journal of advanced computational intelligence and intelligent informatics Vol. 25; no. 1; pp. 40 - 49
• Main Authors: Yan, Guobing, Sun, Qiang, Huang, Jianying, Chen, Yonghong
• Format: Journal Article
• Language: English
• Published: Tokyo Fuji Technology Press Co. Ltd 20.01.2021
• Subjects: Algorithms; Artificial neural networks; Construction accidents & safety; Deep learning; Feature extraction; Helmets; Image processing; Machine learning; Neural networks; Object recognition; Occupational safety; Unmanned aerial vehicles
• ISSN: 1343-0130; 1883-8014
• DOI: 10.20965/jaciii.2021.p0040

Image recognition is one of the key technologies for worker’s helmet detection using an unmanned aerial vehicle (UAV). By analyzing the image feature extraction method for workers’ helmet detection based on convolutional neural network (CNN), a double-channel convolutional neural network (DCNN) model is proposed to improve the traditional image processing methods. On the basis of AlexNet model, the image features of the worker can be extracted using two independent CNNs, and the essential image features can be better reflected considering the abstraction degree of the features. Combining a traditional machine learning method and random forest (RF), an intelligent recognition algorithm based on DCNN and RF is proposed for workers’ helmet detection. The experimental results show that deep learning (DL) is closely related to the traditional machine learning methods. Moreover, adding a DL module to the traditional machine learning framework can improve the recognition accuracy.