This paper presents a system that leverages image processing and deep learning for syringe detection and liquid volume measurement. The main objectives are to develop a robust model for detecting various syringes under different angle orientations and to accurately measure liquid volume within them. The paper details the experimental setup, dataset specifications, and methodologies, including the use of YOLOv8 for object detection, as well as systems for image orientation correction, syringe detection, rubber detection, line detection, and volume calculation. It also introduces a methodology for optimizing the PyTorch model to TensorRT for GPU-embedded devices like the Jetson Orin Nano 8GB, using Quantization Aware Training. This optimization boosts detection speed by 2–3 times with only a 1% accuracy drop, making it suitable for real-time video processing. The system demonstrates 96.89% accuracy in syringe detection and volume measurement across various conditions. The paper concludes with discussions on the integration of image processing and deep learning, performance analysis, and limitations, and includes appendices with confusion matrices for different syringe types.