Speed limiting sign recognition system using histogram of oriented gradients method and K-nearest neighbor classification based on raspberry pi

Nugraheny Wahyu Try, Fitri Utaminingrum
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Abstract

The dominant transportation vehicle in Indonesia is manual transportation. This type of transportation is controlled by the driver himself. Cases of traffic accidents are increasing due to lack of awareness of driving safety and security. The biggest factor in accidents is human error. One accident caused by human error such as a driver who lost speed control, because he ignored the maximum and minimum speed limiting signs. Therefore, the solution for this problem is creating a warning systems that can be used for recognizing maximum and minimum speed limiting signs. The system uses a raspberry pi camera to capture images then be detected and recognized the speed sign. If the system manages to recognize the signs according to the actual conditions traversed by the driver, it will get notification of speed sign figures in the form of sound from the speakers. The study applied the Histogram of Oriented Gradients (HOG) method to obtain the characteristic feature extraction from the sign, then classify it using the K-Nearest Neighbor (K-NN) method. Classification testing using K-NN consist of 650 training data and 48 test data that are comes from six sign types, there are Maks 20 km/h, Max 25 km/h, Max 30 km/h, Max 40 km/h, Max 50 km/h, Min 20 km/h). The average accuracy values is 97.91% for k=1 and 2. Meanwhile, accuracy of k = 3, 4 and 5 have similar value, that is 95.83%. The average time of computing the system to recognize objects 897 milliseconds. The average result of recognition based on the best k value is 97.91%.
基于树莓派直方图定向梯度法和k近邻分类的限速标志识别系统
印尼主要的交通工具是人工运输。这种交通工具是由司机自己控制的。由于缺乏驾驶安全意识,交通事故不断增加。造成事故的最大因素是人为失误。一种事故是由人为失误引起的,比如司机失去了速度控制,因为他忽视了最高和最低速度限制标志。因此,这个问题的解决方案是创建一个警告系统,可以用来识别最高和最低限速标志。该系统使用树莓派相机捕捉图像,然后被检测和识别速度标志。如果系统能够根据驾驶员所经过的实际情况识别出这些标志,它将从扬声器中以声音的形式收到速度标志数字的通知。本研究采用定向梯度直方图(HOG)方法从符号中提取特征特征,然后使用k -最近邻(K-NN)方法对其进行分类。使用K-NN的分类测试包括650个训练数据和48个测试数据,这些数据来自6种符号类型,分别是Maks 20 km/h、Max 25 km/h、Max 30 km/h、Max 40 km/h、Max 50 km/h、Min 20 km/h)。k=1和2时,平均准确率为97.91%。同时,k = 3、4、5的准确率相似,均为95.83%。计算系统识别物体的平均时间为897毫秒。基于最佳k值的识别平均结果为97.91%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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