Neural Networks
Neural Networks
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DOI：
10.1201/9781315273679-16
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发表时间：
2018-10
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影响因子：
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通讯作者：
Faqih Rofii;G. Priyandoko;M. Fanani;A. Suraji
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文献类型：
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作者：
Faqih Rofii;G. Priyandoko;M. Fanani;A. Suraji
【标题：通过基于Yolov4深度神经网络的身份序列检测提高车辆计数精度]基于计算机视觉和人工智能的车辆检测、分类和计数模型不断发展。在这项研究中，我们提出了基于Yolov4的车辆检测，分类和计数模型方法。通过生成每辆车的身份序列号来计算车辆数量。对象被检测和分类，通过显示边界框、类和置信度分数来标记。系统输入是一个视频数据集，它考虑了摄像机位置、光线强度和车辆交通密度。该方法计算了车辆的数量：汽车、摩托车、公共汽车和卡车。模型性能的评估基于混淆矩阵的准确度、精确度和总召回率。数据集检验结果和模型性能参数的计算均获得了最佳的准确度、精密度。当在白天进行模型测试时，摄像机位置在6米的高度，损失500的总召回值为83%，93%和94%。同时，当模型在夜间测试时，获得最低的总准确率、精确率和召回率。摄像机位置在1.5 m的高度，900次损失分别为68%、77%和78%。
[Title: Vehicle Counting Accuracy Improvement By Identity Sequences Detection Based on Yolov4 Deep Neural Networks] Models for vehicle detection, classification, and counting based on computer vision and artificial intelligence are constantly evolving. In this study, we present the Yolov4-based vehicle detection, classification, and counting model approach. The number of vehicles was calculated by generating the serial number of the identity of each vehicle. The object is detected and classified, marked by the display of bounding boxes, classes, and confidence scores. The system input is a video dataset that considers the camera position, light intensity, and vehicle traffic density. The method has counted the number of vehicles: cars, motorcycles, buses, and trucks. Evaluation of model performance is based on accuracy, precision, and total recall of the confusion matrix. The results of the dataset test and the calculation of the model performance parameters had obtained the best accuracy, precision. Total recall values when the model testing was carried out during the day where the camera position was at the height of 6 m and the loss of 500 was 83%, 93%, and 94%. Meanwhile, the lowest total accuracy, precision, and recall were obtained when the model was tested at night. The camera position was at the height of 1.5 m, and 900 losses were 68%, 77%, and 78%.