With the development of infrared detection technology and the improvement of military remote sensing needs, infrared object detection networks with low false alarms and high detection accuracy have been a research focus. However, due to the lack of texture information, the false detection rate of infrared object detection is high, resulting in reduced object detection accuracy. To solve these problems, we propose an infrared object detection network named Dual-YOLO, which integrates visible image features. To ensure the speed of model detection, we choose the You Only Look Once v7 (YOLOv7) as the basic framework and design the infrared and visible images dual feature extraction channels. In addition, we develop attention fusion and fusion shuffle modules to reduce the detection error caused by redundant fusion feature information. Moreover, we introduce the Inception and SE modules to enhance the complementary characteristics of infrared and visible images. Furthermore, we design the fusion loss function to make the network converge fast during training. The experimental results show that the proposed Dual-YOLO network reaches 71.8% mean Average Precision (mAP) in the DroneVehicle remote sensing dataset and 73.2% mAP in the KAIST pedestrian dataset. The detection accuracy reaches 84.5% in the FLIR dataset. The proposed architecture is expected to be applied in the fields of military reconnaissance, unmanned driving, and public safety.

Accelerating deep learning networks in edge computing based on power-efficient and highly parallel FPGA platforms is an important goal. Combined with deep learning theory, an accelerator design method based on the Winograd algorithm for the deep learning object detection model YOLO under the PYNQ architecture is proposed. A Zynq FPGA is used to build the hardware acceleration platform of a YOLO network. The Winograd algorithm is used to improve traditional convolution. In the FPGA, the numerous multiplication operations in the YOLO network are converted into addition operations, reducing the computational complexity of the model. The data of the original model are processed at a low fixed point, reducing the resource consumption of the FPGA. To optimize memory, a buffer pipeline method is proposed, which further improves the efficiency of the designed accelerator. Experiments show that compared with the acceleration of the YOLO model based on GPUs and other FPGA platforms, the proposed method not only optimizes FPGA resource usage but also reduces power consumption to 2.7 W. Additionally, the detection accuracy loss is less than 3%.

Image dehazing is challenging due to the problem of ill-posed parameter estimation. Numerous prior-based and learning-based methods have achieved great success. However, most learning-based methods use the changes and connections between scale and depth in convolutional neural networks for feature extraction. Although the performance is greatly improved compared with the prior-based methods, the performance in extracting detailed information is inferior. In this paper, we proposed an image dehazing model built with a convolutional neural network and Transformer, called Transformer for image dehazing (TID). First, we propose a Transformer-based channel attention module (TCAM), using a spatial attention module as its supplement. These two modules form an attention module that enhances channel and spatial features. Second, we use a multiscale parallel residual network as the backbone, which can extract feature information of different scales to achieve feature fusion. We experimented on the RESIDE dataset, and then conducted extensive comparisons and ablation studies with state-of-the-art methods. Experimental results show that our proposed method effectively improves the quality of the restored image, and it is also better than the existing attention modules in performance.

The properties of the human eye retina, including space-variant resolution and gaze characters, provide many advantages for numerous applications that simultaneously require a large field of view, high resolution, and real-time performance. Therefore, retina-like mechanisms and sensors have received considerable attention in recent years. This paper provides a review of state-of-the-art retina-like imaging techniques and applications. First, we introduce the principle and implementing methods, including software and hardware, and describe the comparisons between them. Then, we present typical applications combined with retina-like imaging, including three-dimensional acquisition and reconstruction, target tracking, deep learning, and ghost imaging. Finally, the challenges and outlook are discussed to further study for practical use. The results are beneficial for better understanding retina-like imaging.

Infrared small target detection for aerial remote sensing is crucial in both civil and military fields. For infrared targets with small sizes, low signal-to-noise ratio, and little detailed texture information, we propose a Res-SwinTransformer with a Local Contrast Attention Network (RSLCANet). Specifically, we first design a SwinTransformer-based backbone to improve the interaction capability of global information. On this basis, we introduce a residual structure to fully retain the shallow detail information of small infrared targets. Furthermore, we design a plug-and-play attention module named LCA Block (local contrast attention block) to enhance the target and suppress the background, which is based on local contrast calculation. In addition, we develop an air-to-ground multi-scene infrared vehicle dataset based on an unmanned aerial vehicle (UAV) platform, which can provide a database for infrared vehicle target detection algorithm testing and infrared target characterization studies. Experiments demonstrate that our method can achieve a low-miss detection rate, high detection accuracy, and high detection speed. In particular, on the DroneVehicle dataset, our designed RSLCANet increases by 4.3% in terms of mAP@0.5 compared to the base network You Only Look Once (YOLOX). In addition, our network has fewer parameters than the two-stage network and the Transformer-based network model, which helps the practical deployment and can be applied in fields such as car navigation, crop monitoring, and infrared warning.

The detection of rotated objects is a meaningful and challenging research work. Although the state-of-the-art deep learning models have feature invariance, especially convolutional neural networks (CNNs), their architectures did not specifically design for rotation invariance. They only slightly compensate for this feature through pooling layers. In this study, we propose a novel network, named LPNet, to solve the problem of object rotation. LPNet improves the detection accuracy by combining retina-like log-polar transformation. Furthermore, LPNet is a plug-and-play architecture for object detection and recognition. It consists of two parts, which we name as encoder and decoder. An encoder extracts images which feature in log-polar coordinates while a decoder eliminates image noise in cartesian coordinates. Moreover, according to the movement of center points, LPNet has stable and sliding modes. LPNet takes the single-shot multibox detector (SSD) network as the baseline network and the visual geometry group (VGG16) as the feature extraction backbone network. The experiment results show that, compared with conventional SSD networks, the mean average precision (mAP) of LPNet increased by 3.4% for regular objects and by 17.6% for rotated objects.