Accurate object detection in aerial images is essential for a wide range of applications. However, detection performance degrades severely in hazy conditions, primarily due to the lack of realistic aerial haze datasets and the mismatch between low-level dehazing and high-level detection tasks. To address these challenges, we propose FSGD-Det, a novel framework for aerial object detection in hazy scenes that improves detection robustness through frequency–amplitude reconstruction. Specifically, we introduce a Physically-guided Haze Generator (PHG) to synthesize realistic hazy images during training, effectively narrowing the domain gap between synthetic and real-world haze. We further design a Frequency Division Dehazing (FDD) module that separately processes high- and low-frequency amplitude components to enhance dehazing quality in a frequency-aware manner. Building upon this, a Frequency Gap Amplification (FGA) module integrates structural cues from the hazy input with fine-grained details recovered from the dehazed image, reconstructing more robust frequency–amplitude representations for detection. Extensive experiments on both synthetic and real-world hazy aerial datasets demonstrate that FSGD-Det consistently outperforms state-of-the-art methods, validating its effectiveness and generalization capability.

Infrared-visible image fusion for object detection (IVIF-OD) aims to utilize complementary information in the two modalities to synthesize new images with richer information to serve object detection. Most existing works focus on how to better fuse pixel-level details while ignoring object-related information required for detection and introducing redundant and object-irrelevant information in the fused images. To address the limitations of previous studies, this paper proposes a diffusion-based denoising fusion for object detection in infrared-visible images, termed DDFD. Specifically, DDFD treats image fusion as a diffusion-based denoising process to generate fused images that are informative yet non-redundant. Since visible imaging is easily affected by adverse conditions, DDFD exploits an image-adaptive enhancement (IAE) module that adaptively improves visible images to achieve better fusion. To extract key fusion features and remove redundancy, DDFD uses an image-aware noise estimator (INE) to determine the noise in the input infrared-visible images for promoting the diffusion denoising network. To take advantage of both the fusion network and object detection network, DDFD jointly optimizes them such that the fusion network can receive object information to improve the fused images, and the improved images can provide high-quality features to enhance object detection performance. Extensive experiments on the M3FD, DroneVehicle, and VEDAI public datasets reveal the superior object detection performance of DDFD and confirm the effectiveness of IVIF-based object detection under challenging weather conditions.

Student pose information can reflect learning status, which is significant in teaching management and evaluation. However, the traditional manual behavior recognition and analysis process is complex and slow, so counting massive classroom data is a difficult task. Therefore, using computer vision technology to accurately recognize student behavior is of great significance to teaching management and evaluation. Besides, the performance of existing behavior recognition methods is limited by problems such as dense objects and occlusion in classroom scenes. To address these issues, we propose an anchor-free object detector based on center keypoints for behavior recognition in classroom scenes. Specifically, we design a multiscale convolution neural networks (CNNs) module to alleviate the scale variation of objects through multiscale receptive fields. The head network is based on the anchor-free detection head architecture and integrates the keypoint heatmaps to accurately extract the center keypoint position through the center pooling module (CPM). the CPM can suppress irrelevant background information and effectively alleviate the missed detection of dense objects. Regressing the distances from the center keypoint of the positive region to the four sides can suppress low-quality bounding boxes so that the detector can accurately locate the object. Furthermore, during the inference stage, the heatmap scores of keypoints and center keypoints are linearly combined as novel confidence to mitigate inaccurate measurement of predicted bounding boxes. The extensive experimental performance comparison on the classroom behavior (CB) and SCB-dataset3 benchmark datasets demonstrate that the proposed behavior recognition method can accurately detect objects in classroom scenes. Compared with other current state-of-the-art methods, the proposed behavior recognition method based on anchor-free object detector is able to achieve good performance.

The zero-watermarking methods provide a means of lossless, which was adopted to protect medical image copyright requiring high integrity. However, most existing studies have only focused on robustness and there has been little discussion about the analysis and experiment on discriminability. Therefore, this paper proposes a trusted robust zero-watermarking scheme for medical images based on Deep convolution neural network (DCNN) and the hyperchaotic encryption system. Firstly, the medical image is converted into several feature map matrices by the specific convolution layer of DCNN. Then, a stable Gram matrix is obtained by calculating the colinear correlation between different channels in feature map matrices. Finally, the Gram matrixes of the medical image and the feature map matrixes of the watermark image are fused by the trained DCNN to generate the zero-watermark. Meanwhile, we propose two feature evaluation criteria for finding differentiated eigenvalues. The eigenvalue is used as the explicit key to encrypt the generated zero-watermark by Lorenz hyperchaotic encryption, which enhances security and discriminability. The experimental results show that the proposed scheme can resist common image attacks and geometric attacks, and is distinguishable in experiments, being applicable for the copyright protection of medical images.

Oriented object detection typically adds an additional rotation angle to the regressed horizontal bounding box (HBB) for representing the oriented bounding box (OBB). However, existing oriented object detectors based on regression angles face inconsistency between metric and loss, boundary discontinuity or square-like problems. To solve the above problems, we propose an anchor-free oriented object detector named PRA-Det, which assigns the center region of the object to regress OBBs represented by the polar radius vectors. Specifically, the proposed PRA-Det introduces a diamond-shaped positive region of category-wise attention factor to assign positive sample points to regress polar radius vectors. PRA-Det regresses the polar radius vector of the edges from the assigned sample points as the regression target and suppresses the predicted low-quality polar radius vectors through the category-wise attention factor. The OBBs defined for different protocols are uniformly encoded by the polar radius encoding module into regression targets represented by polar radius vectors. Therefore, the regression target represented by the polar radius vector does not have angle parameters during training, thus solving the angle-sensitive boundary discontinuity and square-like problems. To optimize the predicted polar radius vector, we design a spatial geometry loss to improve the detection accuracy. Furthermore, in the inference stage, the center offset score of the polar radius vector is combined with the classification score as the confidence to alleviate the inconsistency between classification and regression. The extensive experiments on public benchmarks demonstrate that the PRA-Det is highly competitive with state-of-the-art oriented object detectors and outperforms other comparison methods.

The application of oriented object detection in the field of aerial images has gained substantial attention and made significant progress. However, most one-stage object detectors struggle to extract rotation-invariant features of oriented objects using ordinary convolutions. And the structure of two parallel vision subtasks can result in the inconsistency between the classification and regression. In this article, we propose a Task Integration based on a Rotated convolution kernel Network (TIR-Net) consisting of three modules: selective rotation of the kernel (SRK), regression feature refinement (RFR), and task integration (TI). Specifically, an SRK module enhances classification features by applying rotated convolution kernels selectively, introducing rotational invariance to the features. An RFR module places more emphasis on feature extraction of large aspect ratio objects to improve their perceptibility. A TI module integrates classification and regression features to alleviate the inconsistency between classification and regression. Experimental evaluations on the benchmarks for oriented object detection indicate that our method achieves excellent detection performance.