Review of object detection and hazard identification methods in mine video images

As the coal industry accelerates its transition towards intelligent transformation, video image-based object detection and hazard identification technologies, with advantages of non-contact, visualization, and high real-time performance, are increasingly becoming a key support for enhancing mine safety and production efficiency. This study survey focuses on the innovation and application requirements of video image intelligent analysis technologies in the context of the coal industry’s intelligent transformation. It conducts a systematic study on the current state of intelligent construction in complex mining environments, potential safety hazards, existing advanced target detection and hazard identification methods, as well as technical challenges, supported by practical application cases. The study first examines the current state of construction of mine video surveillance systems, the diversity and complexity of safety hazards, and elucidates the necessity and application value of machine vision technology in underground environments; Subsequently, it compares and analyzes the differences and application value between traditional image processing methods and deep learning-based object detection and recognition technologies in terms of detection accuracy, robustness, and computational efficiency. The study focuses on exploring the adaptability and limitations of mainstream detection models in severe underground mine scenarios such as low illumination, high dust and fog, noise interference, and occlusion. On this basis, combined with the construction and application cases of existing mine video datasets, the role of datasets in model training, performance evaluation, and generalization capability enhancement is analyzed, and the deployment performance of these methods in typical tasks such as coal flow transportation monitoring, violation behavior recognition, operation equipment hazard detection and safety warning in fully mechanized mining faces is further discussed. Finally, the survey summarizes the challenges that current technologies still face, including limited adaptability to complex environments, data imbalance and annotation difficulties, constraints of real-time computing resources, and instability in long-term behavior tracking. Future research directions are outlined, including generalization learning, multi-modal complementary fusion, large-scale semantic modeling, edge-cloud collaborative computing, and data privacy and security protection. Through systematic review and in-depth analysis, this study provides structured theoretical references and technical route support for the continuous innovation and practical implementation of mine video image intelligent analysis technology, thereby enhancing coal mine safety monitoring, hazard warning, and emergency response systems toward higher levels of intelligence and intrinsic safety.