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Multi-object Tracking is an important issue that has been widely investigated in computer vision. However, in practical applications, moving targets are often occluded due to complex changes in the background, which leads to frequent pedestrian ID switches in multi-object tracking. To solve the problem, we present a multi-object tracking algorithm based on FairMOT and Circle Loss. In this paper, HRNet is adopted as the baseline. Then, Polarized Self-Attention is added to HRNet-w32 to obtain weights of helpful information based on its modeling advantages. Moreover, the re-identification branch is optimized, and the Circle Loss is selected as the loss function to acquire more discriminative pedestrian features and to distinguish different pedestrians. The method proposed is assessed on the public MOT17 datasets. The experimental results show that the MOTA score achieves 69.5%, IDF1 reaches 70.0%, and the number of ID switches (IDs) decreases 636 times compared to the TraDes algorithm.

The increase in surface trash salvage tasks has led to problems such as high staff workload, high labor costs, and low work efficiency. A garbage salvage system with autonomous cruising, identification, and detection is designed for this problem. This paper is based on deep learning technology to detect surface garbage and salvage garbage by robotic arm to solve the problem of low intelligence of surface garbage salvage as well as the problem caused by rising labor cost and insufficient labor, and to improve the efficiency of surface garbage salvage task. The system is equipped with a ROS robot operating system and uses lidar to acquire environmental information, realize map construction and autonomous cruise of the salvage vessel, use the YOLO v4 target detection model to detect garbage, and then apply the detected target and location information to the intelligent garbage salvage system. Five common types of garbage on the water surface are collected, and the average detection speed reaches 45 FPS and the average recognition accuracy is up to 88% through experimental verification, meeting the real-time system application.

Temperature control system of infrared heating oven in moisture analyzer is characteristic of nonlinear, time-varying and time-lag. A composite fuzzy control (CFC) method is proposed, which combines improved Bang-Bang control with two-stage intelligent fuzzy control. The control algorithm is implemented by MSP430F5438. When the temperature error e between the desired temperature and actual temperature in heating oven is larger than threshold value, the improved Bang-Bang controller is employed in rapidly reducing the error; to decrease the system overshoot, the basic fuzzy controller is used; to reduce the steady-state error of basic fuzzy controller, the auxiliary fuzzy controller is applied. The steady-state error of improved fuzzy controller for oven temperature is less than 0.5°C, which is better than the Chinese National Standards for moisture content measurement.

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