Master-Slave Convolutional Deep Architecture for Vehicle Identification and Type Classification
Bencheriet Chemesse Ennehar, Bencheriet Samra · Traitement du signal · 2023
Video surveillance of road traffic plays an important role in highway safety and is an important application of intelligent transportation systems.One of the basic applications of intelligent transport systems is the detection and classification of vehicle types.The major problems encountered by these systems are the significant similarity between the vehicles, frequent occlusions on the highway, and low resolution of the surveillance cameras.This paper proposes a novel convolutional neural network architecture called master-slave convolutional deep architecture for vehicle detection and type classification.The basic concept of this architecture is twofold: a.The sequential operation of the two networks where the slave network only works if the master network detects vehicles in the road scene will allow a considerable reduction in the search area for vehicles.It will induce a significant reduction in processing time.b.A combination of deep-shallow neural networks allows the system to share the knowledge gained from the vehicles on two networks.The first (master) shallow learns the shape of vehicles while the second (slave) is responsible for learning all the details of vehicles to distinguish the different classes.The experimental results, performed on 3200 images, have shown that the favorable performance of the proposed CNN architecture allowed us to achieve successful detection with TPR of 92% and TNR of 95% and vehicle type classification with a considerable mean average precision of 93.38% where cars classification gives the highest rate (98.63%).