An Intelligent Vision–Language Framework for Real-Time Environmental Hazard and Safety Risk Detection Using Deep Learning

Abstract

Environmental hazards and safety risks arising from pollution incidents, waste accumulation, chemical spills, fire outbreaks, and other unsafe conditions pose significant challenges to environmental protection and sustainable resource management. Conventional monitoring systems often rely on manual inspection and continuous human supervision, which can lead to delayed responses and reduced effectiveness in large-scale environments. Recent advances in artificial intelligence have enabled automated environmental monitoring; however, many existing approaches require extensive labeled datasets and frequent retraining to adapt to diverse operational conditions. This paper presents an intelligent vision–language framework for real-time environmental hazard detection and safety risk assessment using deep learning and computer vision techniques. The proposed system utilizes the Contrastive Language–Image Pretraining (CLIP) model in a zero-shot learning paradigm to identify environmental hazards from surveillance video streams without task-specific retraining. Visual features extracted from environmental scenes are compared with descriptive textual prompts through cosine similarity-based matching, enabling the recognition of diverse hazardous situations, including waste dumping, smoke emissions, fire incidents, water contamination indicators, and unsafe environmental conditions. To improve operational reliability, a risk intelligence module categorizes detected events into multiple severity levels, namely Safe, Moderate Risk, and High Risk. Confidence-based thresholding and heuristic validation mechanisms are incorporated to minimize false detections and enhance decision-making accuracy. The framework processes surveillance footage in real time and generates automated alerts to facilitate timely intervention and environmental protection measures. Experimental evaluation demonstrates the effectiveness of the proposed approach in accurately identifying environmental hazards across diverse scenarios while maintaining adaptability to previously unseen conditions. The proposed framework offers a scalable and cost-effective solution for intelligent environmental monitoring and sustainable safety management.