Object detection for remote sensing is a fundamental task in image processing of remote sensing; as one of the core components, small or tiny object detection plays an important role. Despite the considerable advancements achieved in small object detection with the integration of CNN and transformer networks, there remains untapped potential for enhancing the extraction and utilization of information associated with small objects. Particularly within transformer structures, this potential arises from the disregard of the complex and the intertwined interplay between spatial context information and channel information during the global modeling of pixel-level information within small objects. As a result, valuable information is prone to being obfuscated and annihilated. To mitigate this limitation, we propose an innovative framework, YOLO-DCTI, that capitalizes on the Contextual Transformer (CoT) framework for the detection of small or tiny objects. Specifically, within CoT, we seamlessly incorporate global residuals and local fusion mechanisms throughout the entire input-to-output pipeline. This integration facilitates a profound investigation into the network’s intrinsic representations at deeper levels and fosters the fusion of spatial contextual attributes with channel characteristics. Moreover, we propose an improved decoupled contextual transformer detection head structure, denoted as DCTI, to effectively resolve the feature conflicts that ensue from the concurrent classification and regression tasks. The experimental results on the Dota, VISDrone, and NWPU VHR-10 datasets show that, on the powerful real-time detection network YOLOv7, the speed and accuracy of tiny targets are better balanced.

This paper introduces a Polarization-Driven Solution (PDS) to enhance the contrast of underwater imagery degraded by light scattering and uneven illumination. Images taken in underwater environments suffer from reduced contrast due to the combined effects of light scattering and non-uniform illumination. We present an Underwater Joint Degradation Model (UJDM) that effectively describes the compounded impacts of scattering and uneven illumination. By exploiting the polarization distinctions between objects and scattered light, we mitigate the deleterious effects of light scattering. Additionally, we leverage the polarization information to persist across changes in illumination, enhancing contrast and detail, especially in darker image regions. Building upon this foundation, our proposed underwater image restoration technique synergistically combines de-scattering with low-light image enhancement. We establish a benchmark database comprising both simulated and authentic underwater polarization images. Experiment results demonstrate that our proposed technique outperforms state-of-the-art underwater image contrast enhancement algorithms, validated through both subjective assessments and objective evaluation metrics. The source code and the datasets are available at https://github.com/polwork/PDS.

Giant cell tumor of bone (GCTB) is potentially malignant with a high risk of recurrence. Conventional GCTB diagnosis is often empirical based on histopathology with low efficiency. Reliable auxiliary diagnosis system can improve the efficiency, but it need accurate GCTB detection. Changes of micro-structure of GCTB tissue can alter the scattering properties that lead to the variation of the polarization state of GCTB tissue. This paper proposed a GCTB detection method by using Mueller matrix polarization microscopic (MMPM) imaging and multi-parameters fusion network. First, a MMPM image dataset of GCTB tissue is set up, and based on this dataset the effectiveness of MMPM imaging in the GCTB detection is verified for the first time. Then a multi-parameters fusion network(MPFN) model is proposed to fuse the deep features and hand craft features, which leverages the advantages of both hand-crafted features and deep learning based systems. Experiment results show that the MPFN outperformed state-of-the-art for GCTB lesions detection.

Recently, the world has witnessed a great increase in drone applications and missions. Drones must be detected quickly, effectively, and precisely when they are being handled illegally. Vision-based anti-drone systems provide an efficient performance compared to radar- and acoustic-based systems. The effectiveness of drone detection is affected by a number of issues, including the drone’s small size, conflicts with other objects, and noisy backgrounds. This paper employs enlarging the effective receptive field (ERF) of feature maps generated from the YOLOv6 backbone. First, RepLKNet is used as the backbone of YOLOv6, which deploys large kernels with depth-wise convolution. Then, to get beyond RepLKNet’s large inference time, a novel LERFNet is implemented. LERFNet uses dilated convolution in addition to large kernels to enlarge the ERF and overcome each other’s problems. The linear spatial-channel attention module (LAM) is used to give more attention to the most informative pixels and high feature channels. LERFNet produces output feature maps with a large ERF and high shape bias to enhance the detection of various drone sizes in complex scenes. The RepLKNet and LERFNet backbones for Tiny-YOLOv6, Tiny-YOLOv6, YOLOv5s, and Tiny-YOLOv7 are compared. In comparison to the aforementioned techniques, the suggested model’s results show a greater balance between accuracy and speed. LERFNet increases the MAP by $$2.8\%$$ 2.8 % , while significantly reducing the GFLOPs and parameter numbers when compared to the original backbone of YOLOv6.

Atmospheric absorption and scattering (e.g., haze) cause degradation in the image quality of outdoor scenes, which affects the image-matching process. The scale-invariant feature transform (SIFT) algorithm is not effective in haze. Edge information is required to enhance the matching process. Utilizing the polarization information expressed by the Stokes vector component S1 with its edge information can improve the keypoint localization in the matching process. In this paper, a novel, to the best of our knowledge, fusion method called polarized intensity-hue-saturation is proposed that uses polarization and depth information by fusion of a polarized haze-removed image with the estimated depth and by applying S1. The instant dehazing method uses polarized images to obtain a haze-removed image and its estimated depth map. The fused image has high spatial details required for enhancing the matching process. The experimental results show that the proposed method outperforms the existing image-matching schemes and improves the conventional SIFT matching method.