Object detection is one of the key tasks in an automatic driving system. Aiming to solve the problem of object detection, which cannot meet the detection speed and detection accuracy at the same time, a real-time object detection algorithm (MobileYOLO) is proposed based on YOLOv4. Firstly, the feature extraction network is replaced by introducing the MobileNetv2 network to reduce the number of model parameters; then, part of the standard convolution is replaced by depthwise separable convolution in PAnet and the head network to further reduce the number of model parameters. Finally, by introducing an improved lightweight channel attention modul—Efficient Channel Attention (ECA)—to improve the feature expression ability during feature fusion. The Single-Stage Headless (SSH) context module is introduced to the small object detection branch to increase the receptive field. The experimental results show that the improved algorithm has an accuracy rate of 90.7% on the KITTI data set. Compared with YOLOv4, the parameters of the proposed MobileYOLO model are reduced by 52.11 M, the model size is reduced to one-fifth, and the detection speed is increased by 70%.

Object detection is one of the key algorithms in automatic driving systems. Aiming at addressing the problem of false detection and the missed detection of both small and occluded objects in automatic driving scenarios, an improved Faster-RCNN object detection algorithm is proposed. First, deformable convolution and a spatial attention mechanism are used to improve the ResNet-50 backbone network to enhance the feature extraction of small objects; then, an improved feature pyramid structure is introduced to reduce the loss of features in the fusion process. Three cascade detectors are introduced to solve the problem of IOU (Intersection-Over-Union) threshold mismatch, and side-aware boundary localization is applied for frame regression. Finally, Soft-NMS (Soft Non-maximum Suppression) is used to remove bounding boxes to obtain the best results. The experimental results show that the improved Faster-RCNN can better detect small objects and occluded objects, and its accuracy is 7.7% and 4.1% respectively higher than that of the baseline in the eight categories selected from the COCO2017 and BDD100k data sets.

The components detection of a failed satellite is an important work in space on-orbit service. However, the current detection methods for failed satellite components do not consider the effects of low illumination and small targets on the detection accuracy of components at the same time, and most of the datasets used are manually designed. This article proposes a detection method based on image enhancement and an improved Faster R-CNN for small components of a failed satellite in low illumination. Firstly, the dataset of failed satellite components dataset containing low illumination scenarios is established in a simulated real space environment. Secondly, an image enhancement based on reflection model and principal component analysis is proposed, which further enhances images while conserving richer details. Finally, an improved Faster R-CNN for small components is proposed. To improve the detection accuracy of small components, the original Faster R-CNN is improved by modifying three modules: backbone, region proposal network, and RoI pooling layer respectively (i.e., M-HRNet, IoU-balanced sampling, RoI Align). Experimental results show that the proposed method can accurately detect all the small components on satellite capture plane, and the detection performance for low illumination and small components is improved significantly compared to the state-of-the-art methods and original Faster R-CNN.

The detection and segmentation of key spacecraft components is a crucial prerequisite for the successful execution of on-orbit capture tasks. However, the existing spacecraft datasets are plagued by several problems. These include a lack of a unified dataset for component detection and segmentation, a lack of consideration of the motion states of the spacecraft, and a lack of consideration of extreme illuminations. These problems hinder the development of related research. In response to the above problems, this paper aims to bridge this gap by releasing a dataset for detecting and segmenting key spacecraft components. In contrast to synthetic images, the proposed dataset is built via hardware-in-the-loop capture. The dataset contains 2 types of spacecrafts, 9 types of key components, 3 types of illuminations (normal illumination, low illumination, and high saturation), and 2 types of motion states (approaching phase and hovering phase). Furthermore, we conduct extensive experiments on Spacecraft-DS with state-of-the-art methods for detection and segmentation tasks and provide comprehensive analysis. In addition, the dataset can also be used to evaluate many computer vision tasks, such as small object detection, etc. Our dataset and pre-trained models will be released at https://github.com/spacecraftds/Spacecraft-DS.

Generative adversarial networks (GANs) are able to produce realistic images. However, GANs may suffer mode collapse in their output data distribution. Here, we theoretically and empirically justify generalizing the GAN framework to multiple discriminators with one generator for improving generative performance. First, a comprehensive perspective is adopted to understand why mode collapse occurs. Second, an array of cooperative realness discriminators is introduced into the GAN framework to combat mode collapse and explore discriminator roles ranging from a formidable adversary to a forgiving teacher. Third, two types of simple yet effective regularization are proposed for generating realistic and diverse images. Experiments on various datasets show the effectiveness of the GAN compared to previous methods in alleviating mode collapse and improving the quality of the generated samples.