# A Comprehensive Analysis of Real-Time Car Safety Belt Detection Using the YOLOv7 Algorithm
## 2 Related Studies  
Considerable research has been dedicated to addressing the safety belt detection challenge, with scholars exploring solutions both inside and outside the vehicle using image-based methods [2]. External detection of safety belts has been proposed by various researchers [3; 7; 11; 12; 13] as an alternative to relying on traffic officers to monitor safety belt usage on highways, given the limited availability of personnel for such tasks. However, this approach still lacks the capability to enforce safety belt usage, as car occupants and drivers can choose to wear seat belts only in areas where external cameras are installed. Most studies focus on detecting the windshield and then identifying the safety belt [11; 12]. Moreover, the safety belt status detection in many of these studies is limited to detecting one person's safety belt, either that of a front passenger or the driver. For instance, [PERSON] et al. [7] proposed a system to detect the front seat passenger's safety belt, while [PERSON] et al. [13], [PERSON] et al. [3], and [PERSON] and [PERSON] [8] developed systems to detect the driver's safety belt. Unfortunately, these studies overlook the safety of other vehicle occupants and concentrate solely on saving one person. The onboard detection of safety belts is seen as a promising solution to the aforementioned issues. [PERSON], [PERSON], and [PERSON] view in-cabin detection of safety belts as a novel approach that can enhance the functionality of new-generation cars [14]. Nevertheless, this method comes with several challenges, such as dealing with constant illumination changes while the car is in motion, potential similarities between the safety belt and the passengers' clothing colour, blurriness in images due to vibration and bumpy roads, and occlusion caused by objects like hands, hair, and clothing [2; 14]. Additionally, the lack of availability of a public dataset poses another challenge. Due to the complexity of these challenges, the topic of safety belt detection remains unsolved, both for internal and external monitoring [14]. Despite this, numerous solutions have been proposed to address these issues. As stated by [PERSON] et al. [15] and [PERSON] et al. [3], many proposed solutions for safety belt detection rely on edge detection algorithms, which can be unstable in varying environments. [PERSON] et al. [12] also concur with this observation and note that some studies use Hugh transform (HT) algorithms in addition to edge detection. These methods fall under the category of traditional algorithms, where the features are handcrafted. Traditional object detection algorithms face challenges related to speed, accuracy, and environmental variations, as pointed out by [PERSON] et al. [16]. Additionally, when processing large datasets, traditional algorithms tend to be inadequate, as highlighted by [PERSON], [PERSON], and [PERSON] [17]. In contrast, deep-learning-based techniques offer a different approach, as depicted in Figure 3. These methods automate feature extraction, providing a more efficient and accurate solution for safety belt detection compared to traditional methods. Table 1 presents the results of various studies, and it is evident that deep-learning-based algorithms outperform traditional methods in terms of accuracy, supporting [PERSON] et al. 's [16] submissions. Traditional algorithms fall short in accuracy compared to deep learning techniques. However, it is worth mentioning that [PERSON] [2] claims to have achieved an accuracy of over 95% with his shape modelling algorithm, which is a notable achievement. Among the deep-learning-based algorithms, YOLOv5, as presented by [PERSON] and [PERSON] [8] in 2023, achieved the highest accuracy. Our study utilizes the YOLOv7 algorithm for safety belt detection due to its numerous benefits. YOLO is an open-source single-stage object detector that is continuously being researched and improved. While YOLOv8 and YOLO-NAS are new variants, most researchers have opted for YOLOv7, leading us to select YOLOv7 for our study.