Today’s garbage classification is not fully automated and mostly relies on manual labor. In order to efficiently classify garbage and increase resource re-usability, society needs a modern solution. The development of artificial intelligence, especially in object detection, provides an excellent opportunity to develop real-time accurate garbage detection. This project is about real-time garbage detection using YOLOX and is used to detect seventeen categories of garbage. After the camera detects the garbage, it will automatically identify and analyze the type and specific the position of the garbage, which will be automatically picked up by a mechanical jaws. The datasets used in this paper consists of 5000 images with label. With the officially supported TensorRT optimization and acceleration technology, the model can be used in low-cost embedded devices and can detect garbage in near real-time without the need for a stable Internet connection. Our evaluation and comparison of these models includes some key metrics such as mean accuracy (mAP), inference time per frame, floating point operations(FLOPs), and the number of parameters of the model. The test results show that YOLOX’s mAP _0.5:0.95 can exceed 97% and the inference speed can exceed 32 fps. The performance far exceeds that of other YOLO series, with high accuracy and speed in complex environments, making it highly applicable to today’s garbage classification needs.

The LSTM neural network is often employed for time-series data prediction due to its strong nonlinear mapping capability and memory effect, allowing for better identification of complex data characteristics. However, the large computational workload required by neural networks can result in longer prediction times, making deployment on time-sensitive embedded devices challenging. To address this, TensorRT, a software development kit for NVIDIA hardware platforms, offers optimized network structures and reduced inference times for deep learning inference applications. Though TensorRT inference is GPU-based like other deep learning frameworks, TensorRT outperforms comparable frameworks in terms of inference speed. In this paper, we compare the inference time consumption and prediction deviation of various approaches on CPU, GPU, and TensorRT, while also exploring the effects of different quantization approaches. Our experiments demonstrate the accuracy and inference latency of the same model on the FPGA development board PYNQ-Z1 as well, though the best results were obtained using NVIDIA Jetson Xavier NX. The results show an approximately 50× improvement in inference speed compared to our previous technique, with only a 0.2% increase in Mean Absolute Percentage Error (MAPE). These works highlight the effectiveness and efficiency of TensorRT in reducing inference times, making it an excellent choice for time-sensitive embedded device deployments that require high precision and low latency.

In view of the problem that the traditional motor test system cannot directly test the transient parameters of the motor and the dynamic arbitrary load loading requirements during motor loading, as well as the high cost of implementation, this research uses STM32+FPGA as the core to form the main control of the motor test system unit, combining the superior control performance of the ARM processor and the high-speed data processing advantages of FPGA. FPGA and STM32 are controlled by the FSMC bus communication and data ping-pong algorithm. Using this method, a small-size control core board in the motor test system is manufactured. It can be embedded in the existing traditional dynamometer system to improve the dynamometer transient parameter test and the dynamic motor loading performance. The experimental results show that the system can basically meet the requirements of the motor transient test and dynamic loading, and can achieve the fastest data refresh rate of 1 ms when measuring the motor’s speed and torque, as well as arbitrary waveform loading within a 100 M sampling frequency, with a loading error of 0.8%. It satisfies the motor transient test and dynamic loading requirements.