BMS College of Engineering, Bangalore, Karnataka, India.
Department of Chemical Engineering, College of Biological and Chemical Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia.
Comput Intell Neurosci. 2022 Sep 2;2022:4423744. doi: 10.1155/2022/4423744. eCollection 2022.
Spectrum of applications in computer vision use object detection algorithms driven by the power of AI and ML algorithms. State of art detection models like faster Region based convolutional Neural Network (RCNN), Single Shot Multibox Detector (SSD), and You Only Look Once (YOLO) demonstrated a good performance for object detection, but many failed in detecting small objects. In view of this an improved network structure of YOLOv4 is proposed in this paper. This work presents an algorithm for small object detection trained using real-time high-resolution data for porting it on embedded platforms. License plate recognition, which is a small object in a car image, is considered for detection and an auditory speech signal is generated for detecting fake license plates. The proposed network is improved in the following aspects: Training the classifier by using positive data set formed from the core patterns of an image. Training YOLOv4 by the features obtained by decomposing the image into low frequency and high frequency. The resultant values are processed and demonstrated via a speech alerting signals and messages. This contributes to reducing the computation load and increasing the accuracy. Algorithm was tested on eight real-time video data sets. The results show that our proposed method greatly reduces computing effort while maintaining comparable accuracy. It takes 45 fps to detect one image when the input size is 1280 × 960, which could keep a real-time speed. Proposed algorithm works well in case of tilted, blurred, and occluded license plates. Also, an auditory traffic monitoring system can reduce criminal attacks by detecting suspicious license plates. The proposed algorithm is highly applicable for autonomous driving applications.
计算机视觉中的应用范围广泛，涉及到各种对象检测算法，这些算法都受益于人工智能和机器学习算法的强大功能。先进的检测模型，如更快的基于区域的卷积神经网络（RCNN）、单发多盒检测器（SSD）和单次检测（YOLO），在对象检测方面表现出了良好的性能，但在检测小物体方面存在许多问题。针对这个问题，本文提出了一种改进的 YOLOv4 网络结构。本文提出了一种使用实时高分辨率数据进行训练的小物体检测算法，并将其移植到嵌入式平台上。本文考虑使用汽车图像中的小车牌照识别来进行检测，并生成听觉语音信号来检测伪造的车牌。所提出的网络在以下几个方面进行了改进：使用从图像的核心模式形成的正数据集来训练分类器。通过将图像分解为低频和高频来训练 YOLOv4。对得到的数值进行处理，并通过语音警报信号和消息进行演示。这有助于减少计算负载并提高准确性。该算法在八个实时视频数据集上进行了测试。结果表明，我们提出的方法在保持可比准确性的同时，大大减少了计算量。当输入大小为 1280×960 时，检测一张图像需要 45 fps，可以保持实时速度。所提出的算法在车牌倾斜、模糊和遮挡的情况下效果良好。此外，听觉交通监控系统可以通过检测可疑车牌来减少犯罪攻击。所提出的算法非常适用于自动驾驶应用。
