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.

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With the increase in the amount of 3D point cloud data and the wide application of point cloud registration in various fields, the question of whether it is possible to quickly extract the key points of registration and perform accurate coarse registration has become a question to be urgently answered. In this paper, we proposed a novel semantic segmentation algorithm that enables the extracted feature point cloud to have a clustering effect for fast registration. First of all, an adaptive technique was proposed to determine the domain radius of a local point. Secondly, the feature intensity of the point is scored through the regional fluctuation coefficient and stationary coefficient calculated by the normal vector, and the high feature region to be registered is preliminarily determined. In the end, FPFH is used to describe the geometric features of the extracted semantic feature point cloud, so as to realize the coarse registration from the local point cloud to the overall point cloud. The results show that the point cloud can be roughly segmented based on the uniqueness of semantic features. The use of a semantic feature point cloud can make the point cloud have a very fast response speed based on the accuracy of coarse registration, almost equal to that of using the original point cloud, which is conducive to the rapid determination of the initial attitude.

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