To address the challenges of low detection rate and high missed detection rate of military aircraft in current complex remote sensing data, and to meet the requirements of real-time detection and easy deployment of models, this paper introduces DET-YOLO, an innovative detection model. Firstly, to tackle the issue of reduced accuracy in identifying small targets amidst intricate backgrounds, a novel feature extraction component, C2f_DEF, was devised. This module replaced all existing C2f components within YOLOv8n, thereby significantly enhancing the model's ability to cope with complicated environmental contexts. Secondly, to achieve the functionality of easy deployment of the model, some deep structures were simplified to make the model more lightweight. Afterwards, to further improve the model's ability to handle complex backgrounds and dense environments in remote sensing images, and to improve the model's detection accuracy for military aircraft, the DAT module was embedded in the model. Finally, this paper also optimized the loss function and reg_max to further reduce computational costs while improving the detection accuracy of the model. To verify the effectiveness and strong universality of DET-YOLO, extensive experimental verification was conducted on three publicly available datasets, namely MAR20, NWPU VHR-10, and NEU-DET. On the MAR20 dataset, compared to other advanced models, DET-YOLO achieved the highest mAP0.5 (namely 94.7%) with only 80 training epochs, while meeting lightweight and real-time requirements. While on the other two datasets, DET-YOLO also achieved the best detection performance.

Due to the uncertain, diverse, and light-attenuating characteristics of the underwater environment, underwater images have low contrast and unclear problems. This paper proposes a histogram equalization algorithm based on optimized adaptive image quadruple segmentation and cropping (AQSCHE). Compared with the traditional histogram equalization underwater image enhancement algorithm, this algorithm introduces histogram quadruple segmentation and cropping technology. Using the exposure value and segmentation point calculation formula that optimizes the distribution range of the histogram, perform quadruple segmentation on the image to obtain a more refined histogram. The adaptive histogram clipping is realized by constructing the clipping parameter z to adjust the contrast and brightness of the image. The original image is enhanced by the equalization of the sub-histogram and the histogram of each channel. Finally, the simulation experiments verify the enhancement effect of the proposed algorithm AQSCHE on underwater images. The processed underwater image has higher contrast, clearer and more natural in subjective evaluation, and has better visual effect; in the image objective evaluation indicators, information entropy (Entropy), peak signal to noise ratio (PSNR), structural similarity index (SSIM) and universal color image quality evaluator (UCIQE), etc., this algorithm also outperforms other common algorithms such as HE and CLAHE.

Surface defect detection is critical in manufacturing magnetic tiles to improve production yield. However, existing detection methods are difficult to use to accurately locate and segment small defects on magnetic tile images, because these defects always occupy extremely low proportions of images, and their visual features are difficult to identify, which means their feature representation for defect detection is quite weak. To address this issue, we propose an effective and feasible detection algorithm for small defects on magnetic tile surfaces. Firstly, based on local structure similarity of magnetic tile surfaces, the image is decomposed into low-rank and sparse matrices for estimating possible defect regions. To accurately locate defect areas while filtering out stains, textures, and noises, the sparse matrix is binarized and used for connected components analysis. Then, pixel values in the defect area are normalized, and the Retinex theory is applied to enhance the contrast between defects and background. Finally, an optimal threshold is determined by an automatic threshold segmentation method to segment the defect areas and edges precisely. Experimental results on a number of magnetic tile samples containing different types of defects demonstrated that the proposed algorithm outperforms the existing methods in terms of all evaluation metrics, showing broad industrial application prospects.

Detecting small defects against a complex surface is highly challenging but crucial to ensure product quality in industry sectors. However, in the detection performance of existing methods, there remains a huge gap in the localization and segmentation of small defects with limited sizes and extremely weak feature representation. To address the above issue, this paper presents a weighted matrix decomposition model (WMD) for small defect detection against a complex surface. Firstly, a weighted matrix is constructed based on texture characteristics of RGB channels in the defect image, which aims to improve contrast between defects and the background. Based on the sparse and low-rank characteristics of small defects, the weighted matrix is then decomposed into low-rank and sparse matrices corresponding to the redundant background and defect areas, respectively. Finally, an automatic threshold segmentation method is used to obtain the optimal threshold and accurately segment the defect areas and their edges in the sparse matrix. The experimental results show that the proposed model outperforms state-of-the-art methods under various quantitative evaluation metrics and has broad industrial application prospects.