Due to the strong reflection property of materials with smooth surfaces like ceramic and metal, it will cause saturation and the highlight phenomenon in the image when taking pictures of those materials. In order to solve this problem, a new algorithm which is based on reflection component separation (RCS) and priority region filling theory is designed. Firstly, the specular pixels in the image are found by comparing the pixel parameters. Then, the reflection components are separated and processed. However, for ceramic, metal and other objects with strong specular highlight, RCS theory will change color information of highlight pixels due to larger specular reflection component. In this situation, priority region filling theory was used to restore the color information. Finally, we implement 3D experiments on objects with strong reflecting surfaces like ceramic plate, ceramic bottle, marble pot and yellow plate. Experimental results show that, with the proposed method, the highlight caused by the strong reflecting surface can be well suppressed. The highlight pixel number of ceramic plate, ceramic bottle, marble pot and yellow plate, is decreased by 43.8 times, 41.4 times, 33.0 times, and 10.1 times. Three-dimensional reconstruction results show that highlight areas were significantly reduced.

Disease is one of the primary threats to maize growth. Currently, maize disease detection is mainly conducted in laboratories, making it difficult to promptly respond to diseases. To enable detection in the field, a lightweight model is required. Therefore, this paper proposes a lightweight model, YOLOv8-GO, optimized from the YOLOv8 (You Only Look Once version 8) model. The Global Attention Mechanism was introduced before the SPPF (Spatial Pyramid Pooling Fast) layer to enhance the model’s feature extraction capabilities without significantly increasing computational complexity. Additionally, Omni-dimensional Dynamic Convolution was employed to optimize the model’s basic convolutional structure, bottleneck structure, and C2f (Faster Implementation of CSP (Cross Stage Partial) Bottleneck with two convolutions) module, improving feature fusion quality and reducing computational complexity. Compared to the base model, YOLOv8-GO achieved improvements across all metrics, with mAP@50 increasing to 88.4%, a 2% gain. The computational complexity was 9.1 GFLOPs, and the model could run up to 275.1 FPS. YOLOv8-GO maintains a lightweight design while accurately detecting maize disease targets, making it suitable for application in resource-constrained environments.