Intelligent Remote Sensing Vehicle Classification using YOLOv8 and Probabilistic Neural Network

Vehicle detection and classification represent critical tasks within intelligent traffic monitoring systems, yet traditional methods often pose computational challenges and limitations when adapting to varying data collection methods. This study provides a unique technique for vehicle recognition and classification in aerial image sequences. The suggested model involves six steps. Initially, all images undergo preprocessing to remove noise and boost brightness. At that point, foreground components are extracted using segmentation. Following segmentation, the YOLOv8 algorithm is used to identify vehicles. On the identified vehicles, feature extraction is then carried out using Learned Arrangements of Three Patch Codes (LATCH), Binary Robust Independent Elementary Features (BRIEF), and Accelerated-KAZE (AKAZE). Particle swarm optimization (PSO) was used to maximize the features. We classify via the Probabilistic Neural Network (PNN) classifier. Positive results are shown by experimental results on three datasets; on the AU-AIR dataset, the proposed model achieves an accuracy of 91%, and on the Roundabout dataset, 94%. Furthermore, a comparative analysis with other traditional approaches is provided to thoroughly evaluate the model’s performance.