Abstract: In order to improve the quality stability and achieve simultaneous real-time monitoring of droplet-melting pool multi-feature information during the hot-wire laser metal deposition (HW-LMD) process, this study adopts a high-precision real-time monitoring method based on a high-dynamic vision camera combined with a YOLO v8 deep learning neural network. The dynamic changes in the HW-LMD process are captured by a camera, and the transition mode and melt pool behaviour are monitored synchronously using the YOLO v8 neural network, which firstly determines whether the deposition process is a stable liquid bridge transition or not, and then extracts the key point information of the melt pool dimensions in the liquid bridge transition mode. The results show that the YOLO v8 neural network has high accuracy in detecting the transition mode and melt pool key point information of the deposition process, with an accuracy rate of 98.8% and 99.9%, respectively, and an average error of 4.1% for the melt pool width, and the inference time is only 12 ms per frame on average, which meets the demand for real-time monitoring of the HW-LMD process.