In the rapidly developing drone industry, drone use has led to a series of safety hazards in both civil and military settings, making drone detection an increasingly important research field. It is difficult to overcome this challenge with traditional object detection solutions. Based on YOLOv8, we present a lightweight, real-time, and accurate anti-drone detection model (EDGS-YOLOv8). This is performed by improving the model structure, introducing ghost convolution in the neck to reduce the model size, adding efficient multi-scale attention (EMA), and improving the detection head using DCNv2 (deformable convolutional net v2). The proposed method is evaluated using two UAV image datasets, DUT Anti-UAV and Det-Fly, with a comparison to the YOLOv8 baseline model. The results demonstrate that on the DUT Anti-UAV dataset, EDGS-YOLOv8 achieves an AP value of 0.971, which is 3.1% higher than YOLOv8n’s mAP, while maintaining a model size of only 4.23 MB. The research findings and methods outlined here are crucial for improving target detection accuracy and developing lightweight UAV models.

Air Quality Index (AQI) is the crucial foundation for measuring air quality, which reflects the influence of air quality on people’s health and life to a certain extent. In this paper, a hybrid AQI prediction model based on Convolutional Neural Network (CNN) and Attention Gate Unit (AGU) is proposed to deal with the problems of the “vanishing gradient” and “exploding gradient” of Recurrent Neural Network (RNN). AGU is a new model proposed in this paper to embed the attention mechanism and Data Adjustment Module (DAM) into the gated unit. The attention mechanism enhances the learning ability of the gated unit, and the DAM makes the gated unit more sensitive to historical data learning. In this model, CNN plays a role in extracting features from time series data. AGU can make differentiated learning of historical data and finally produce prediction results. The model evaluation indexes used in the experiments are Mean Absolute Error (MAE), Mean Square Error (MSE), and R Squared (R2). The experimental results show that the overall performance of the AQI prediction model based on CNN-AGU is superior to that of other models by comparing with the other nine models on the same data set.

Due to the diversity of aircraft target scale and interference of background strong scattering in synthetic aperture radar (SAR) images, it is a challenge for target detection tasks. In response to these problems, this paper proposes a new SAR image aircraft target detection model named EST-YOLOv5s. The proposed model integrates the Efficient Channel Attention (ECA) mechanism into the C3 module of the backbone network, which enhances the scattering features of aircraft targets and suppresses irrelevant background information without increasing the number of parameters. Secondly, replace the bottleneck module in the last C3 module in the backbone network with the Swin Transformer Block. By using the shifted window partitioning approach to obtain the global perception ability, the problem of missed detection of small objects is improved. Finally, the Task-Specific Context Decoupling (TSCODE) head is used to balance the relationship between classification and regression so that different contextual details can be better utilized. In this paper, the SAR Aircraft Detection Dataset (SADD) is used as the experimental data set to compare with the baseline model YOLOv5s. The experimental outcomes indicate that the recall of the EST-YOLOv5s model reached 94.2%, the precision reached 97.3%, and the mAP@50 reached 97.8%, which were 2.3%, 1.7%, and 1.7% higher than YOLOv5s respectively. Furthermore, our model also meets the real-time requirements in terms of speed and exhibits strong anti-interference ability.

Image denoising, as an essential component of image pre-processing, effectively reduces noise interference to enhance image quality, a factor of considerable research importance. Traditional denoising methods often lead to the blurring of image details and a lack of realism at the image edges. To deal with these issues, we propose an image denoising algorithm named Residual structure and Cooperative Attention mechanism based on Generative Adversarial Networks (RCA-GAN). This algorithm proficiently reduces noise while focusing on preserving image texture details. To maximize feature extraction, this model first employs residual learning within a portion of the generator’s backbone, conducting extensive multi-dimensional feature extraction to preserve a greater amount of image details. Secondly, it introduces a simple yet efficient cooperative attention module to enhance the representation capacity of edge and texture features, further enhancing the preservation of intricate image details. Finally, this paper constructs a novel loss function—the Multimodal Loss Function—for the network training process. The experimental results were evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) as evaluation metrics. The experimental results demonstrate that the proposed RCA-GAN image denoising algorithm has increased the average PSNR from 24.71 dB to 33.76 dB, achieving a 36.6% improvement. Additionally, the average SSIM value has risen from 0.8451 to 0.9503, indicating a 12.4% enhancement. It achieves superior visual outcomes, showcasing the ability to preserve image texture details to a greater extent and excel in edge preservation and noise suppression.

Synthetic Aperture Radar (SAR) is an active microwave sensor that has attracted widespread attention due to its ability to observe the ground around the clock. Research on multi-scale and multi-category target detection methods holds great significance in the fields of maritime resource management and wartime reconnaissance. However, complex scenes often influence SAR object detection, and the diversity of target scales also brings challenges to research. This paper proposes a multi-category SAR image object detection model, CCDS-YOLO, based on YOLOv5s, to address these issues. Embedding the Convolutional Block Attention Module (CBAM) in the feature extraction part of the backbone network enables the model’s ability to extract and fuse spatial information and channel information. The 1 × 1 convolution in the feature pyramid network and the first layer convolution of the detection head are replaced with the expanded convolution, Coordinate Conventional (CoordConv), forming a CRD-FPN module. This module more accurately perceives the spatial details of the feature map, enhancing the model’s ability to handle regression tasks compared to traditional convolution. In the detector segment, a decoupled head is utilized for feature extraction, offering optimal and effective feature information for the classification and regression branches separately. The traditional Non-Maximum Suppression (NMS) is substituted with the Soft Non-Maximum Suppression (Soft-NMS), successfully reducing the model’s duplicate detection rate for compact objects. Based on the experimental findings, the approach presented in this paper demonstrates excellent results in multi-category target recognition for SAR images. Empirical comparisons are conducted on the filtered MSAR dataset. Compared with YOLOv5s, the performance of CCDS-YOLO has been significantly improved. The mAP@0.5 value increases by 3.3% to 92.3%, the precision increases by 3.4%, and the mAP@0.5:0.95 increases by 6.7%. Furthermore, in comparison with other mainstream detection models, CCDS-YOLO stands out in overall performance and anti-interference ability.