The recognition of incorrect fastening of seat belts is significant in passenger and driver safety for the automotive industry and public health. It should be made sure that the passenger’s seat belt is not only fastened but also correctly fastened across the body so that the passenger is adequately protected in the event of an accident. Current technology employs the buckle effect sensor, which merely solves the buckling detection problem, but there is no reliable solution for the correct positioning of the seat belt. Additionally, computer vision-based systems are still incapable of recognizing the incorrect positioning of seat belts when the training is performed by employing the subjects out of the fleet. Considering this fact, in this study, we propose a novel solution that employs a vision-based incorrect fastening seat belt detector to perform automatic vertical height adjustment independent from drivers and passengers for the fleet vehicles. We recognize the incorrect positioning of the seat belt inside the car by the acceptable distance of the seat belt from the neck of drivers or passengers to avoid neck injuries and the deaths caused by neck cuts. An extensive benchmarking is performed by comparing the three CNN architectures such as; DenseNet121, GoogLeNet (Inception-v3), ResNet50 with respect to sensitivity, specificity, precision, false-positive rate, false-negative rate, F1 score, and accuracy. Additionally, training and validation loss curves and accuracy curves are plotted for all the models. Later, the three models are evaluated with a precision-recall (PR) curve at the end. According to the results, the DenseNet121 achieved the highest classification accuracy among the tested models with 99.95%. This paper includes information about the proposed system elements, registration of data, elaboration of data, program algorithm, testing the system in the lab, and on the vehicle.

识别不正确系安全带对汽车行业和公共健康的乘客和驾驶员安全具有重要意义。应确保乘客的安全带不仅系好，而且在身体上正确系紧，以便在发生事故时充分保护乘客。目前的技术采用扣环效应传感器，仅解决扣环检测问题，对于安全带的正确定位还没有可靠的解决方案。此外，当通过雇用车队外的受试者进行培训时，基于计算机视觉的系统仍然无法识别安全带的错误位置。考虑到这一事实，在本研究中，我们提出了一种新颖的解决方案，该解决方案采用基于视觉的错误扣紧安全带检测器来为车队车辆执行独立于驾驶员和乘客的自动垂直高度调节。我们通过安全带与驾驶员或乘客颈部的可接受距离来识别车内安全带的错误位置，以避免颈部受伤和颈部割伤造成的死亡。通过比较三种 CNN 架构来执行广泛的基准测试，例如：DenseNet121、GoogLeNet (Inception-v3)、ResNet50 在敏感性、特异性、精确度、假阳性率、假阴性率方面，我们通过安全带与驾驶员或乘客颈部的可接受距离来识别车内安全带的错误位置，以避免颈部受伤和颈部割伤造成的死亡。通过比较三种 CNN 架构来执行广泛的基准测试，例如：DenseNet121、GoogLeNet (Inception-v3)、ResNet50 在敏感性、特异性、精确度、假阳性率、假阴性率方面，我们通过安全带与驾驶员或乘客颈部的可接受距离来识别车内安全带的错误位置，以避免颈部受伤和颈部割伤造成的死亡。通过比较三种 CNN 架构来执行广泛的基准测试，例如：DenseNet121、GoogLeNet (Inception-v3)、ResNet50 在敏感性、特异性、精确度、假阳性率、假阴性率方面，F 1 分数，和准确性。此外，还为所有模型绘制了训练和验证损失曲线以及准确度曲线。之后，最后用精确召回 (PR) 曲线对这三个模型进行评估。根据结果​​，DenseNet121 在测试模型中达到了最高的分类准确率，达到 99.95%。本文包括有关提议的系统元素、数据注册、数据详细说明、程序算法、在实验室和车辆上测试系统的信息。