Frontier exploration of image recognition in fuel spray diagnostics: Hybrid deep learning models and multimodal data fusion

Owing to its high detection accuracy and real-time processing capabilities, image recognition technology has become an indispensable tool for extracting spray morphological characteristics and analyzing dynamic evolution processes in combustion systems. This review systematically summarizes recent advances in image recognition technology, with a focus on its applications in multiphase flow coupling and detailed feature extraction within spray environments, while also providing a forward-looking discussion of current challenges and future trends. Conventional methods are limited by weak anti-interference capability, low feature extraction efficiency, and poor generalization. Deep learning techniques have been increasingly adopted to enhance boundary segmentation precision and quantitative feature parameter extraction. However, a major challenge remains in adapting these technologies to complex environments, as most existing models struggle to balance lightweight design with measurement accuracy—a critical barrier to real-time engineering applications. Emerging approaches, including hybrid CNN–Transformer architectures and novel Mamba-based models such as UltraLight_VM_UNet, have demonstrated significant potential. The model achieves a segmentation accuracy of up to 95.43 % mIoU for complex sprays, while reducing computational costs to just 0.05M parameters and 0.33 GFLOPs. These advancements significantly improve robustness and generalization under noisy and dynamic spray conditions. Future developments are expected to focus on computational efficiency, robustness in extreme scenarios, and more effective global–local feature fusion, thereby paving the way for real-time diagnostic applications in combustion systems.