The increasingly frequent sand-dust weather in the inland areas seriously affects outdoor vision applications, especially autonomous vehicles and security monitoring. To moderate the image's colour cast and poor contrast caused by sand-dust weather, an effective approach is proposed in this study to enhance the sand-dust images. First, the original degraded image's colour cast is corrected by a new colour balance and compensation formula, which compensates the blue and green channel information through numerous yellow channel information caused by sand-dust scattering before white balance. Next, in order to avoid the new colour deviation, the corrected image is converted from the RGB colour space to the HSV colour space and use the CLAHE to enhance the V component to improve the contrast. Then, a nonlinear gain function is defined to further adaptively sharpen the V component to enhance image details. Finally, the S component is stretched to improve image saturation. The extensive qualitative and quantitative evaluation shows that this method can improve the image edge clarity and contrast, restore good colour fidelity for all sand-dust images tested. The verification also proves that this method is of much significance in improving the feature point extraction and the target detection results in the sand-dust weather.

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To solve the object detection task in the harsh sandy environment, this paper proposes a lightweight sandy vegetation object detection algorithm based on attention mechanism. We reduce the number of model parameters by lightweight design of the anchor-free object detection algorithm model, thereby reducing the model inference time and memory cost. Specifically, the algorithm uses a lightweight backbone network to extract features, and uses linear interpolation in the neck network to achieve multi-scale. Model algorithm compression is performed by depthwise separable convolution in the head network. At the same time, the channel attention mechanism is added to the model to further optimize the algorithm. Experiments have proved the superiority of the algorithm, the mAP in the training effect is 76%, and the prediction time per frame is 0.0277 seconds. It realizes the efficiency and accuracy of the algorithm operation in the desert environment.

As the cathode of lithium-ion batteries, carbon material has been the focus of research. At present, diverse investigations have been carried out on the lithium convergence behavior in the carbon material family. As a new carbon material, multilayer fullerenes have been shown in various experimental studies to have a high discharge rate as an electrode, indicating that onion-like carbon has the potential to release energy quickly. Materials and mechanical scientists are increasingly interested in lithium-ion batteries. In this paper, the molecular dynamics (MD) method was used to simulate the absorption of lithium ions by multilayer fullerenes. A model of five layers of fullerenes was established to compare the lithium-ion absorption rates of multiple layers of fullerenes at different lithium-ion concentrations. The effects of the lithium-ion diffusion rate on the results were considered. In addition, the effects of the number of lithium ions, the velocity, and the layer number of multilayer fullerenes on the structural behavior and stress were investigated thoroughly when the multilayer fullerenes adsorbed lithium ions.

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