# An Improved YOLOv5s-Based Helmet Recognition Method for Electric Bikes
## 2 Related Work  
In recent years, a variety of researchers have proposed different algorithms for the detection of safety helmets. [PERSON] et al. [18] proposed a CNN-based MTL method to identify and track individual motorcycles and detect helmet usage. They introduced the HELMET dataset, which consists of 91,000 annotated frames from 10,006 motorcycles in Myanmar. [PERSON] et al. [19] developed a real-time computer vision-based system for helmet detection utilizing the YOLOv5x architecture. Their objective was to achieve high accuracy in detecting helmets at construction sites, even under low-light conditions. [PERSON] et al. [20] proposed a safety helmet detection method based on a deep convolutional network. Their approach involved decoding video monitoring data, extracting YUV images, and applying a carefully designed convolutional neural network model for detection purposes. [PERSON] et al. [21], on the other hand, utilized TensorFlow's image recognition framework to train a deep learning Single Shot MultiBox Detector (SSD) model and identified the helmet usage accurately. To solve the problems of safety helmet detection within complex factory environments, [PERSON] et al. [22] developed a method that combined multi-feature fusion and Support Vector Machine (SVM) classification techniques. [PERSON] et al. [23] designed a double-channel convolutional neural network (DCNN) model to enhance conventional image processing methods for detecting workers' helmets. Their focus was specifically on extracting image features using convolutional neural networks (CNN). [PERSON] et al. [24] proposed an automatic helmet detection method for motorcyclists based on deep learning. They utilized an improved YOLOv5 detector to detect motorcycles from video surveillance and further determine if the motorcyclists were wearing helmets. They introduced a new motorcycle helmet dataset (HFUT-MH) that surpasses existing datasets in terms of size and comprehensiveness. In a study conducted by [PERSON] et al. [25], the problems of resource wastage and low monitoring efficiency in Container Freight Stations (CFS) caused by manual safety helmet detection were solved. They proposed a novel approach using the Broad Learning System (BLS) optimized by a Genetic Algorithm (GA) as an image recognition classifier. The GA-BLSaccurately identified CFS workers without safety helmets in video footage, thus achieving early warnings. Compared to the initial BLS and other methods such as Support Vector Machine (SVM), the GA-BLS achieves lower error rates and reduces operation time. This study provides an effective solution for enhancing safety helmet detection in CFS, optimizing resource utilization, and monitoring efficiency. [PERSON] et al. (2016) introduced SAS-YOLOv3-Tiny, an innovative multi-scale safety helmet detection algorithm. This algorithm achieved an improved accuracy and model complexity trade-off while surpassing the original algorithm in terms of various metrics and computational efficiency. Lastly, [PERSON] et al. (2017) presented an automated system for real-time identification of motorcyclists without helmets from traffic surveillance videos. The authors compiled a custom dataset and proposed a two-stage classifier. The first stage extracts motorcycles, followed by a helmet identification stage. Two algorithms, one based on hand-crafted features and the other using a deep CNN, were presented for rider classification. The CNN-based model achieved higher accuracy, while the feature-based model enabled faster detection. These studies have introduced a series of methods and concepts for detecting motorcycle riders without helmets. However, certain challenges still exist. Table 1 lists the limitations of these approaches.