Cold spray technology, as an emerging surface engineering technique, effectively prepares hard coatings by high-speed projection of powder materials onto substrates at relatively low temperatures. The principal advantage of this technology lies in its ability to rapidly deposit coatings without significantly altering the properties of the substrate or powder materials. Carbon-based materials, especially carbides and diamond, etc., are renowned for their exceptional hardness and thermal stability, which make them indispensable in industrial applications requiring materials with high wear resistance and durability at elevated temperatures. This review elucidates the fundamental principles of cold spray technology, the key components of the equipment, and the properties and applications of hard coatings. The equipment involved primarily includes spray guns, powder feeders, and gas heaters, while the properties of the coatings, such as mechanical strength, corrosion resistance, and tribological performance, are discussed in detail. Moreover, the application of this technology in preparing metal matrix composite (MMC) materials with carbon-based reinforcements, including tungsten carbide, boron carbide, titanium carbide, and diamond, are particularly emphasized, showcasing its potential to enhance the performance of tools and components. Finally, this article outlines the challenges and prospects faced by cold spray technology, highlighting the importance of material innovation and process optimization. This review provides researchers in the fields of materials science and engineering with a comprehensive perspective on the application of cold spray technology in MMC materials with carbon-based reinforcements to drive significant improvements in coating performance and broaden the scope of its industrial applications.

Traffic light detection and recognition are crucial for enhancing the security of unmanned systems. This study proposes a YOLOv5-based traffic light-detection algorithm to tackle the challenges posed by small targets and complex urban backgrounds. Initially, the Mosaic-9 method is employed to enhance the training dataset, thereby boosting the network’s ability to generalize and adapt to real-world scenarios. Furthermore, the Squeeze-and-Excitation (SE) attention mechanism is incorporated to improve the network. Moreover, the YOLOv5 algorithm’s loss function is optimized by substituting it with Efficient Intersection over Union loss (EIoU_loss), which addresses issues like missed detection and false alarms. Experimental results demonstrate that the model trained with this enhanced network achieves an mAP (mean average precision) of 99.4% on a custom dataset, which is 6.3% higher than that of the original YOLOv5, while maintaining a detection speed of 74 f/s. Therefore, this algorithm offers higher detection accuracy and effectively meets real-time operational requirements. The proposed method has a strong application potential, and can be widely used in the field of automatic driving, assisted driving, etc. Its application is not only of great significance in improving the accuracy and speed of traffic sign detection, but also can provide technical support for the development of intelligent transportation systems.

The traffic light detection and recognition are crucial for enhancing the security of unmanned systems. This study proposes a YOLOv5-based traffic light detection algorithm to tackle the challenges posed by small targets and complex urban backgrounds. Initially, the mosaic-9 method is employed to enhance the training dataset, thereby boosting the network’s ability to generalize and adapt to real-world scenarios. Furthermore, the network incorporates the Squeezed-and-Excitation (SE) attention mechanism to improve. Moreover, the YOLOv5 algorithm’s loss function has been optimized by substituting it with EIoU_loss, which addresses issues like missed detection and false alarms. Experimental results demonstrate that the model , trained with this enhanced network, achieves a 99.4% mAP on a custom dataset, which is 6.3% higher than the original YOLOv5, while maintaining a detection speed of 74 f/s. Therefore, this algorithm offers higher detection accuracy and effectively meets real-time operational requirements.