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.