Deep Learning Advances in Vision-Based Traffic Accident Anticipation: A Comprehensive Review of Methods,Datasets,and Future Directions

Traffic accident prediction and detection are critical for enhancing road safety,and vision-based traffic accident anticipation (Vision-TAA) has emerged as a promising approach in the era of deep learning.This paper reviews 147 recent studies,focusing on the application of supervised,unsupervised,and hybrid deep learning models for accident prediction,alongside the use of real-world and synthetic datasets.Current methodologies are categorized into four key approaches: image and video feature-based prediction, spatiotemporal feature-based prediction, scene understanding,and multimodal data fusion.While these methods demonstrate significant potential,challenges such as data scarcity,limited generalization to complex scenarios,and real-time performance constraints remain prevalent. This review highlights opportunities for future research,including the integration of multimodal data fusion, self-supervised learning,and Transformer-based architectures to enhance prediction accuracy and scalability.By synthesizing existing advancements and identifying critical gaps, this paper provides a foundational reference for developing robust and adaptive Vision-TAA systems,contributing to road safety and traffic management.

A super-resolution reconstruction method for lightweight building images based on an expanding feature modulation network

This study proposes a lightweight method for building image super-resolution using a Dilated Contextual Feature Modulation Network (DCFMN). The process includes obtaining high-resolution images, down-sampling them to low-resolution, enhancing the low-resolution images, constructing and training a lightweight network model, and generating super-resolution outputs. To address challenges such as regular textures and long-range dependencies in building images, the DCFMN integrates an expansion separable modulation unit and a local feature enhancement module. The former employs multiple expansion convolutions equivalent to a large kernel to efficiently aggregate multi-scale features while leveraging a simple attention mechanism for adaptivity. The latter encodes local features, mixes channel information, and ensures no additional computational burden during inference through reparameterization. This approach effectively resolves the limitations of existing lightweight super-resolution networks in modeling long-range dependencies, achieving accurate and efficient global feature modeling without increasing computational costs, and significantly improving both reconstruction quality and lightweight efficiency for building image super-resolution models.