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During the packaging process, it is essential to detect the steel stamping characters inside the box to identify any missing or repeated characters. Currently, manual detection suffers from low efficiency and a high false detection rate. To address these challenges, a steel stamping character recognition and analysis system based on machine vision has been developed. The enhanced YOLOv7 detection method was employed for character identification, complemented by a statistical analysis approach to achieve automated judgment and detection. To address the issue of size disparity between large and small characters, a small size anchor box and a larger detection head were integrated. Furthermore, modifications were made to the output structure of the YOLOv7 prediction network to enhance multi-scale detection capabilities. The inclusion of the location attention convolution module bolstered global feature extraction, thereby enhancing the detection accuracy of similar characters. Moreover, the utilization of a hash table was used to improve the efficiency of mapping steel stamping character recognition sequences. The experimental results demonstrate that the enhanced model achieves an accuracy of 99.83%, with a processing efficiency of 10.5 ms per single frame. These findings align with the performance criteria for automatic recognition and analysis of steel stamping characters.

Diabetic foot ulcers (DFUs), a serious and increasingly common chronic issue among diabetics, often do not respond well to generalized treatment strategies. Hypoxia and the overexpression of reactive oxygen species (ROS), resulting in inflammatory dysregulation and subsequent imbalance in macrophage phenotypes, are critical factors contributing to the prolonged non‐healing of DFU wounds. These two issues often interact in a continuous, problematic cycle. Presently, there is a lack of comprehensive strategies aimed at addressing both of these factors simultaneously to interrupt this detrimental cycle. Herein, an immunomodulatory hydrogel (PHG2) is developed for reshaping the hostile DFU microenvironment. The engineered PHG2 not only removes excess internally‐produced ROS but also generates O2, with its efficiency further boosted by local hyperthermia (42.5 °C) activated by near‐infrared light. Through both in vitro and in vivo studies, along with transcriptomic assessment, it is confirmed that PHG2 disrupts the detrimental feedback loop between ROS and inflammation while also lowering the M1/M2 macrophage ratio. Such discoveries contribute to a significant enhancement in the healing process of injuries that undergo a gradual increase in movement, covering wounds from the back, mouth, to the foot. Ultimately, this method provides an easy, safe, and highly effective solution for treating DFUs. [ABSTRACT FROM AUTHOR]