To solve the problems that the existing sorting equipment cannot effectively identify and sort damaged Camellia oleifera seeds and traditional manual sorting of damaged Camellia oleifera seeds is inefficient and slow, in this paper, a damaged Camellia oleifera seeds detection method based on YOLOv5, coordinate attention, and weighted bidirectional feature pyramid network was designed. In this study, according to the actual requirements, firstly, the Coordinate Attention module (CA) was added to the YOLOv5 algorithm to improve the detection precision of damaged Camellia oleifera seeds in stacked Camellia oleifera seeds. Secondly, the network structure was optimized and the weighted bi-directional feature pyramid network (BiFPN) was added. The module integrates multi-scale features from top to bottom to reduce the missed detection of slightly damaged Camellia seeds. The final experimental results show that compared with the original YOLOv5 model, the detection precision of the improved model YOLOV5-CB is improved by 6.1%, reaching 92.4%, and the mean Average Precision (mAP) is also improved from 87.7% to 93.4%, the average detection time of a single Camellia seeds image is 6.4ms, which meet the requirements of precision and real-time in practical application.

Optical coherence tomography (OCT) image processing can provide information about the uterine cavity structure, such as endometrial surface roughness, which is important for the diagnosis of uterine cavity lesions. The accurate segmentation of uterine cavity OCT images is a key step of OCT image processing. We proposed an EA-UNet-based image segmentation model that uses a U-Net network structure with a multi-scale attention mechanism to improve the segmentation accuracy of uterine cavity OCT images. The E(ECA-C) module introduces a convolutional layer combined with the ECA attention mechanism instead of max pool, reduces the loss of feature information, enables the model to focus on features in the region to be segmented, and suppresses irrelevant features to enhance the network’s feature-extraction capability and learning potential. We also introduce the A (Attention Gates) module to improve the model’s segmentation accuracy by using global contextual information. Our experimental results show that the proposed EA-UNet can enhance the model’s feature-extraction ability; furthermore, its MIoU, Sensitivity, and Specificity indexes are 0.9379, 0.9457, and 0.9908, respectively, indicating that the model can effectively improve uterine cavity OCT image segmentation and has better segmentation performance.

In the factory nursery, qualified seedlings can be used to replant unqualified seedlings or missing seedlings in the seedling tray through automatic transplanters. Due to the random positions of unqualified and missing seedlings, the end effector of the automatic replanting machine spends substantial time shuttling between the supply tray and the target tray to complete the replanting task. Therefore, we proposed a fast path planning method based on improved particle swarm optimization and compared it with the fixed sequence method and genetic algorithm in experiments with different replanting numbers in different tray types. The experiment shows that the improved particle swarm optimization algorithm and genetic algorithm can shorten the length of the replantation path by about 20% compared with the fixed sequence method, and the running time of the improved particle swarm optimization algorithm is 57.63% less than the genetic algorithm on average. The replanting path optimization method based on improved particle swarm optimization designed in this research can significantly optimize the length and time of the replanting path of the seedling tray, improve the efficiency of the replanting operation, and meet the real-time requirements.

Abstract. Traditional underground garage lighting control mainly adopts manual control mode, which usually keep most of the lamps bright for a long time. This way causes a great waste of electricity. To solve the problem, the underground garage lighting intelligent control system is put forward. On the basis of making a rational model for the garage, with the help of a new type of intelligent lighting controller, the classic A * search algorithm and the existing license plate recognition technology, the system not only can avoid long light phenomenon and control the lighting in the garage reasonably, but also can induce vehicles in the garage to arrive at the designated parking space through the lighting. A specific example of taking advantage of the A * search algorithm to calculate a path from the entrance to the designated parking space in the underground garage was taken, the simulation results are demonstrated. This shows that the system is feasible and practical.