Survey on Image-Based Vehicle Detection Methods

Abstract

Vehicle detection is essential for real-world applications such as road surveillance, intelligent transportation systems, and autonomous driving, where high accuracy and real-time performance are critical. However, achieving robust detection remains challenging due to scene complexity, occlusion, scale variation, and varying lighting conditions. Over the past two decades, numerous studies have been proposed to address these issues. This study presents a comprehensive and structured survey of image-based vehicle detection methods, systematically comparing classical machine learning techniques based on handcrafted features with modern deep learning approaches. Deep learning methods are categorized into one-stage detectors (e.g., YOLO, SSD, FCOS, CenterNet), two-stage detectors (e.g., Faster R-CNN, Mask R-CNN), transformer-based detectors (e.g., DETR, Swin Transformer), and GAN-based methods, highlighting architectural trade-offs concerning speed, accuracy, and practical deployment. We analyze widely adopted performance metrics from recent studies, evaluate characteristics and limitations of popular vehicle detection datasets, and explicitly discuss technical challenges, including domain generalization, environmental variability, computational constraints, and annotation quality. The survey concludes by clearly identifying open research challenges and promising future directions, such as efficient edge deployment strategies, multimodal data fusion, transformer-based enhancements, and integration with Vehicle-to-Everything (V2X) communication systems.