Search

Abstract An increasing number of researchers are using deep learning technology to classify and process garbage in rural areas, and have achieved certain results. However, the existing garbage detection models still have problems such as high complexity, missed detection of small targets, low detection accuracy and poor real-time performance. To address these issues, we train a model and apply it to garbage classification and detection in rural areas. In general, we propose an attention combination mechanism based on the YOLOv5 algorithm to build a better backbone network structure, add a new small object detection layer in the head network to enhance the model's ability to detect small objects, adopt the CIoU loss function to optimize the output prediction bounding box, and choose the Adam optimization algorithm to train the model. Our proposed YOLOv5s-CSS model detects a single garbage image in 0.021 s with a detection accuracy of 96.4%. Compared with the YOLOv5 algorithm and the classic detection algorithm, the improved algorithm has better detection speed and detection accuracy. At the same time, the complexity of the network model is reduced to a certain extent, which can meet the requirements of real-time detection of rural domestic garbage.

It is well-known that geomagnetic fields have multiple components or parameters, and that these geomagnetic parameters are related to each other. In this paper, a parameter selection method is proposed, and this paper mainly discusses the correlation of geomagnetic field parameters for geomagnetic navigation technology. For the correlation analysis between geomagnetic parameters, the similarity calculation of the correlation coefficient is firstly introduced for geomagnetic navigation technology, and the grouped results are obtained by data analysis. At the same time, the search algorithm (Hex-path algorithm) is used to verify the correlation analysis results. The results show the same convergent state for the approximate correlation coefficient. In other words, the simulation results are in agreement with the similarity calculation results.

An aggregation or assembly of Ag triangular nanoplates (Ag TNPs) can cause dramatic changes in their optical properties, which is widely used in applications in the field of sensing. The assembly forms of nanoparticles are crucial for obtaining sensitive sensing signals, but it is unknown what kind of assembly dominates the aggregated Ag TNPs in aqueous solutions. Herein, using thiram-induced Ag TNP aggregation as a model, six different assembly models were established, including three planar (side-by-side, side-to-tip, and tip-to-tip) assemblies and three tridimensional (plane-to-plane, plane-to-tip, and plane-to-side) assemblies. The corresponding optical properties were then investigated. Both theoretical and experimental findings indicate that three-dimensional assemblies, especially plane-to-plane assembly, dominate the Ag TNPs aggregation solution, causing a blue shift of the absorption spectrum. Analysis of charge distribution patterns in Ag TNPs indicates that such a blue shift is caused by the electrostatic repulsive force in plane-to-plane assembly. Thus, we propose a simple colorimetric method for thiram detection using Ag TNPs as an indicator. The method exhibits a selective and sensitive response to thiram with a limit of detection of 0.13 μM in the range of 0.2–0.5 μM, as well as excellent performance in real samples like wheat.