Fabric defect detection is very important in the textile quality process. Current deep learning algorithms are not effective in detecting tiny and extreme aspect ratio fabric defects. In this paper, we proposed a strong detection method, Priori Anchor Convolutional Neural Network (PRAN-Net), for fabric defect detection to improve the detection and location accuracy of fabric defects and decrease the inspection time. First, we used Feature Pyramid Network (FPN) by selected multi-scale feature maps to reserve more detailed information of tiny defects. Secondly, we proposed a trick to generate sparse priori anchors based on fabric defects ground truth boxes instead of fixed anchors to locate extreme defects more accurately and efficiently. Finally, a classification network is used to classify and refine the position of the fabric defects. The method was validated on two self-made fabric datasets. Experimental results indicate that our method significantly improved the accuracy and efficiency of detecting fabric defects and is more suitable to the automatic fabric defect detection.

Multispectral pedestrian detection via visible and thermal image pairs has received widespread attention in recent years. It provides a promising multi-modality solution to address the challenges of pedestrian detection in low-light environments and occlusion situations. Most existing methods directly blend the results of the two modalities or combine the visible and thermal features via a linear interpolation. However, such fusion strategies tend to extract coarser features corresponding to the positions of different modalities, which may lead to degraded detection performance. To mitigate this, this paper proposes a novel and adaptive cross-modality fusion framework, named Hierarchical Attentive Fusion Network (HAFNet), which fully exploits the multispectral attention knowledge to inspire pedestrian detection in the decision-making process. Concretely, we introduce a Hierarchical Content-dependent Attentive Fusion (HCAF) module to extract top-level features as a guide to pixel-wise blending features of two modalities to enhance the quality of the feature representation and a plug-in multi-modality feature alignment (MFA) block to fine-tune the feature alignment of two modalities. Experiments on the challenging KAIST and CVC-14 datasets demonstrate the superior performance of our method with satisfactory speed.

Detecting tiny objects in multimodal Red-Green-Blue-Thermal (RGBT) imagery is a critical challenge in computer vision, particularly in surveillance, search and rescue, and autonomous navigation. Drone-based scenarios exacerbate these challenges due to spatial misalignment, low-light conditions, occlusion, and cluttered backgrounds. Current methods struggle to leverage the complementary information between visible and thermal modalities effectively. We propose COXNet, a novel framework for RGBT tiny object detection, addressing these issues through three core innovations: i) the Cross-Layer Fusion Module, fusing high-level visible and low-level thermal features for enhanced semantic and spatial accuracy; ii) the Dynamic Alignment and Scale Refinement module, correcting cross-modal spatial misalignments and preserving multi-scale features; and iii) an optimized label assignment strategy using the GeoShape Similarity Measure for better localization. COXNet achieves a 3.32% mAP₅₀ improvement on the RGBTDronePerson dataset over state-of-the-art methods, demonstrating its effectiveness for robust detection in complex environments.

In recent years, multispectral object detection technology has gained widespread attention due to its significant performance in detecting objects with similar colors or textures in complex environments. The mainstream methods adopt the fusion of visible light (RGB) and thermal images to make up for the shortcomings of a single modality, improving the detection accuracy. However, most methods fail to take the inherent differences between modalities into account. RGB and thermal images differ in object attributes, which may lead to inconsistent contribution of each modality to the fusion features. Hence, equally feeding them into the feature extractor will limit the expressiveness of the fusion features. In order to reasonably utilize complementary information cues of each modality, an effective cross-modality feature fusion network is proposed in this letter. It comprises a spectral-spatial enhancing module (SSE) and a feature fusion module via Transformer (FFT).For dual-modality data, in the aerial dataset, our model’s detection accuracy metrics, mAP50 and mAP, are respectively improved by 1.2% and 0.9% compared to the best dual-modality network.Comprehensive experiments on both ground and aerial datasets demonstrate that our approach outperforms existing methods. The achievements are of great significance for enhancing the robustness and accuracy of multispectral object detection.

Hyperspectral remote sensing images provide rich spectral information about land surface features and are widely used in fields such as environmental monitoring, disaster assessment, and land classification. However, effectively leveraging the spectral information in hyperspectral images remains a significant challenge. In this paper, we propose a hyperspectral pansharpening method based on ratio transformation and residual networks, which significantly enhances both spatial details and spectral fidelity. The method generates an initial image through ratio transformation and refines it using a residual attention network. Additionally, specialized loss functions are designed to preserve both spatial and spectral details. Experimental results demonstrate that, when evaluated on the EO-1 and Chikusei datasets, the proposed method outperforms other methods in terms of both visual quality and quantitative metrics, particularly in spatial detail clarity and spectral fidelity. This approach effectively addresses the limitations of existing technologies and shows great potential for high-resolution remote sensing image processing applications.

Digital images are captured by various fixed and mobile cameras, compressed with traditional and novel techniques, transmitted through different communication channels, and stored in various storage devices. Distortions can occur at each stage of the image acquisition, processing, transmission and storage pipeline, resulting in loss of perceptual information and degradation of quality. Therefore, image quality assessment is becoming increasingly important in monitoring image quality and ensuring the reliability of image processing systems. And as the most widely applicable and usable of the image quality assessment fields, a large number of learning-based no-reference quality assessment studies have been conducted in recent years. In this survey, we provide an up-to-date and comprehensive review of these studies. Specifically, this paper presents recent advances in the field of deep learning-based no-reference quality assessment and provides an overview of benchmark databases for deep learning-based no-reference quality assessment tasks as well as assessment metrics and the backbone networks commonly used in quality assessment tasks.