Robust Vehicle Detection Based on Improved You Look Only Once

Abstract

Vehicle detection is still challenging for intelligent transportation systems (ITS) to achieve satisfactory performance. The existing methods based on one stage and two-stage have intrinsic weakness in obtaining high vehicle detection performance. Due to advancements in detection technology, deep learning-based methods for vehicle detection have become more popular because of their higher detection accuracy and speed than the existing algorithms. This paper presents a robust vehicle detection technique based on Improved You Look Only Once (RVD-YOLOv5) to enhance vehicle detection accuracy. The proposed method works in three phases; in the first phase, the K-means algorithm performs data clustering on datasets to generate the classes of the objects. Subsequently, in the second phase, the YOLOv5 is applied to create the bounding box, and the Non-Maximum Suppression (NMS) technique is used to eliminate the overlapping of the bounding boxes of the vehicle. Then, the loss function CIoU is employed to obtain the accurate regression bounding box of the vehicle in the third phase. The simulation results show that the proposed method achieves better results when compared with other state-of-art techniques, namely Lightweight Dilated Convolutional Neural Network (LD-CNN), Single Shot Detector (SSD), YOLOv3 and YOLOv4 on the performance metric like precision, recall, mAP and F1-Score. The simulation and analysis are carried out on PASCAL VOC 2007, 2012 and MS COCO 2017 datasets to obtain better performance for vehicle detection. Finally, the RVD-YOLOv5 obtains the results with an mAP of 98.6% and Precision, Recall, and F1-Score are 98%, 96.2% and 97.09%, respectively.

Fusion of Deep Sort and Yolov5 for Effective Vehicle Detection and Tracking Scheme in Real-Time Traffic Management Sustainable System

“…He et al [21] proposed a two-stage SPP-net. The CNN was 20 times quicker than R-CNN [13] with similar accuracy. It can also obtain inputs of non-fixed size, disregarding the size of the region of interest.…”

“…On the other hand, one one-stage approach has better ongoing execution exactness and speed on MS COCO and PASCAL VOC datasets [24,25]. Various two-stage object detection algorithms have been proposed, like R-CNN, Fast R-CNN, Faster R-CNN, and Mask R-CNN, for the necessity of speed and accuracy [12][13][14][15]. He et al [21] proposed a two-stage SPP-net.…”

“…Deep learning methods are more effective and accurate, and they perform better in complex environments to detect objects [11]. Convolutional neural network (CNN)-based deep learning techniques for object recognition [12][13][14] are categorized into two types: the two-stage method and one-stage method [15][16][17]. In the one-stage method, deep-learning-based object detection, You Only Look Once (YOLO) v5 achieves high accuracy, speed, and high reliability in object detection compared to the other one-stage-based detectors [18].…”