Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights

Abstract

Minimizing human intervention in engines, such as traffic lights, through automatic applications and sensors has been the focus of many studies. Thus, Deep Learning (DL) algorithms have been studied for traffic signs and vehicle identification in an urban traffic context. However, there is a lack of priority vehicle classification algorithms with high accuracy, fast processing, and a lightweight solution. For filling those gaps, a vehicle detection system is proposed, which is integrated with an intelligent traffic light. Thus, this work proposes (1) a novel vehicle detection model named Priority Vehicle Image Detection Network (PVIDNet), based on YOLOV3, (2) a lightweight design strategy for the PVIDNet model using an activation function to decrease the execution time of the proposed model, (3) a traffic control algorithm based on the Brazilian Traffic Code, and (4) a database containing Brazilian vehicle images. The effectiveness of the proposed solutions were evaluated using the Simulation of Urban MObility (SUMO) tool. Results show that PVIDNet reached an accuracy higher than 0.95, and the waiting time of priority vehicles was reduced by up to 50%, demonstrating the effectiveness of the proposed solution.

Scale-Invariant Multidirectional License Plate Detection with the Network Combining Indirect and Direct Branches

“…Moreover, the ALPD module, the indirect detection branch, and the whole detection network have almost the same vehicle detection performance as the vanilla SSD [36]. This way, it proves our method can continuously improve the license plate detection performance while maintaining the vehicle detection performance [43][44][45][46][47].…”

An Analysis of Image Features Extracted by CNNs to Design Classification Models for COVID-19 and Non-COVID-19

“…The use of pre-trained networks with the data set ImageNet is widespread in the literature [ 28 , 65 ]. Thus, pre-trained CNNs with this data set are applied to extract feature vectors in the identification of COVID-19.…”

Light Field Image Quality Enhancement by a Lightweight Deformable Deep Learning Framework for Intelligent Transportation Systems

“…Bl t * (x, y, s) are interspersed as Bl t * (x, y, s) ↑ to the resolution (H, W, N). Thus, the details of Bl t * (x, y, s) ↑ are restored as F r3d (•), forming the intermediate LF in Equation (7).…”

“…Currently, the Light Field (LF) imaging [1] area has been explored by many studies [2,3] in the field of Virtual Reality (VR), Augmented Reality (AR) and different industrial applications, such as the commercial plenoptic cameras. In addition, different image-based solutions are used in Intelligent Transportation Systems (ITSs) for several applications [4][5][6][7][8][9][10], which use different machine learning techniques. ITS solutions aim to improve safety, mobility and efficiency of transport services, and to accomplish these goals, visual information plays an important role in the development of these services.…”