To address the challenges of small target detection in UAV aerial images—such as difficulty in feature extraction, complex background interference, high miss rates, and stringent real-time requirements—this paper proposes an innovative model series named SRTSOD-YOLO, based on YOLO11. The backbone network incorporates a Multi-scale Feature Complementary Aggregation Module (MFCAM), designed to mitigate the loss of small target information as network depth increases. By integrating channel and spatial attention mechanisms with multi-scale convolutional feature extraction, MFCAM effectively locates small objects in the image. Furthermore, we introduce a novel neck architecture termed Gated Activation Convolutional Fusion Pyramid Network (GAC-FPN). This module enhances multi-scale feature fusion by emphasizing salient features while suppressing irrelevant background information. GAC-FPN employs three key strategies: adding a detection head with a small receptive field while removing the original largest one, leveraging large-scale features more effectively, and incorporating gated activation convolutional modules. To tackle the issue of positive-negative sample imbalance, we replace the conventional binary cross-entropy loss with an adaptive threshold focal loss in the detection head, accelerating network convergence. Additionally, to accommodate diverse application scenarios, we develop multiple versions of SRTSOD-YOLO by adjusting the width and depth of the network modules: a nano version (SRTSOD-YOLO-n), small (SRTSOD-YOLO-s), medium (SRTSOD-YOLO-m), and large (SRTSOD-YOLO-l). Experimental results on the VisDrone2019 and UAVDT datasets demonstrate that SRTSOD-YOLO-n improves the mAP@0.5 by 3.1% and 1.2% compared to YOLO11n, while SRTSOD-YOLO-l achieves gains of 7.9% and 3.3% over YOLO11l, respectively. Compared to other state-of-the-art methods, SRTSOD-YOLO-l attains the highest detection accuracy while maintaining real-time performance, underscoring the superiority of the proposed approach.

Hyperspectral images reconstruction focuses on recovering the spectral information from a single RGBimage. In this paper, we propose two advanced Generative Adversarial Networks (GAN) for the heavily underconstrained inverse problem. We first propose scale attention pyramid UNet (SAPUNet), which uses U-Net with dilated convolution to extract features. We establish the feature pyramid inside the network and use the attention mechanism for feature selection. The superior performance of this model is due to the modern architecture and capturing of spatial semantics. To provide a more accurate solution, we propose another distinct architecture, named W-Net, that builds one more branch compared to U-Net to conduct boundary supervision. SAPUNet and scale attention pyramid WNet (SAPWNet) provide improvements on the Interdisciplinary Computational Vision Lab at Ben Gurion University (ICVL) datasetby 42% and 46.6%, and 45% and 50% in terms of root mean square error (RMSE) and relative RMSE, respectively. The experimental results demonstrate that our proposed models are more accurate than the state-of-the-art hyperspectral recovery methods

Image generation technology is currently one of the popular directions in computer vision research, especially regarding infrared imaging, bearing critical applications in the military field. Existing algorithms for generating infrared images from visible images are usually weak in perceiving the salient regions of images and cannot effectively highlight the ability to generate texture details in infrared images, resulting in less texture details and poorer generated image quality. In this study, a cycle generative adversarial network method based on gradient normalization was proposed to address the current problems of poor infrared image generation, lack of texture detail and unstable models. First, to address the problem of limited feature extraction capability of the UNet generator network that makes the generated IR images blurred and of low quality, the use of the residual network with better feature extraction capability in the generator was employed to make the generated infrared images highly defined. Secondly, in order to solve issues concerning severe lack of detailed information in the generated infrared images, channel attention and spatial attention mechanisms were introduced into the ResNet with the attention mechanism used to weight the generated infrared image features in order to enhance feature perception of the prominent regions of the image, helping to generate image details. Finally, to tackle the problem where the current training models of adversarial generator networks are insufficiently stable, which leads to easy collapse of the model, a gradient normalization module was introduced in the discriminator network to stabilize the model and render it less prone to collapse during the training process. The experimental results on several datasets showed that the proposed method obtained satisfactory data in terms of objective evaluation metrics. Compared with the cycle generative adversarial network method, the proposed method in this work exhibited significant improvement in data validity on multiple datasets.

Aiming at the problems of small target detection feature extraction difficulty, complex background interference, high missed detection rate and high real-time requirements in aerial images of UAVs, this paper proposes an innovative SRTSOD-YOLO series model based on YOLO11 model. This model integrates a Multi-scale Feature Complementary Aggregation Module (MFCAM) in the backbone network. MFCAM is committed to al-leviating the problem of difficult feature extraction caused by the loss of small target information with the increase of network depth. Through the combination of channel and spatial attention mechanism and convolutional feature extraction of different scales, the position of small objects in the image can be effectively captured. In addition, we design a new neck architecture, called Gated Activation Convolutional Fusion Pyramid Network (GAC-FPN), which efficiently highlights important features and suppresses irrelevant background information during multi-scale feature fusion. GAC-FPN uses three main strategies to enhance small target detection performance: adding a detector head with a small receptive field while deleting the detector head with the original largest receptive field, making full use of large-scale features, and using gated activation convolutional module. Aiming at the imbalance of positive and negative samples in the image, the adaptive threshold focus loss function is used to replace the original binary cross-entropy loss function in the detection head, which speeds up the convergence speed of the network. In addition, in order to adapt to different application scenarios, we generate different versions of SRTSOD-YOLO by setting different widths and depths of network modules: small model (SRTSOD-YOLO-n), smaller model (SRT-SOD-YOLO-s), medium model (SRTSOD-YOLO-m), and large model (SRT-SOD-YOLO-l). The experimental results on two datasets, VisDrone2019 and UAVDT, showed that SRTSOD-YOLO-n improved the mAP50 index by 3.1% and 1.2% compared to YOLO11n, and SRTSOD-YOLO-l improved the mAP50 index by 7.9% and 3.3% compared to YOLO11l, respectively. Compared with other existing methods, SRT-SOD-YOLO-l achieves the highest detection accuracy while maintaining real-time performance, demonstrating the superiority of the proposed method.