(untitled) (Informatica, 2025)
In response to the dual challenges of insufficient generalization of traditional image processing methods and high computational complexity of deep learning models in industrial visual scenes, this study proposes a two-stage solution integrating object detection and a deep learning scheme. The scheme employs a modified Darknet-19 backbone with depthwise separable convolutions and channel rearrangement mechanisms for multi-scale feature fusion, significantly improving computational efficiency while maintaining accuracy. Experiments on a dataset of 4,000 industrial water level images and 10,000 encoding samples showed that the research method achieved 97% pixel-level accuracy and 5 mm positioning error in water level detection, outperforming suboptimal models by 12%. For encoding recognition, it reached a 97% character recognition rate with only 5% false detection rate. In multi-task scenarios, system interference was reduced to 0.12, with 62% increased video memory usage and stable 25 ms edge latency. The multi-scale photometric transformation achieved a lighting invariance index of 0.93 and improved SNR by 8.7 dB. Lightweight deployment yielded a computational density of 1.26 GMACs/mm² and a 72-hour failure rate below 0.1%. This work provides an accuracy-efficiency balanced solution for industrial vision systems, with applications in smart security and intelligent manufacturing. Future work will focus on adaptive calibration and dynamic pruning for enhanced deployment adaptability.