Ship instance segmentation in synthetic aperture radar (SAR) images is a hard and challenging task, which not only locates ships but also obtains their shapes with pixel-level masks. However, in ocean SAR images, because of the consistent reflective intensities of ships, the appearances of different ships are similar, thus making it far too difficult to distinguish ships when they are in densely packed groups. Especially when ships have incline directions and large aspect ratios, the horizontal bounding boxes (HB-Boxes) used by all the instance-segmentation networks that we know so far inevitably contain redundant backgrounds, docks, and even other ships, which mislead the following segmentation. To solve this problem, a novel ship instance-segmentation network, called SRNet, is proposed with rotated bounding boxes (RB-Boxes), which are taken as the foundation of segmentation. Along the directions of ships, the RB-Boxes can surround the ships tightly, but a minor deviation will corrupt the integrity of the ships’ masks. To improve the performance of the RB-Boxes, a dual feature alignment module (DAM) was designed to obtain the representative features with the direction and shape information of ships. On account of the difference between the classification task and regression task, two different sampling location calculation strategies were used in two convolutional kernels of the DAM, making these locations distributed dynamically on the ships’ bodies and along the ships’ boundaries. Moreover, to improve the effectiveness of training, a new adaptive Intersection-over-Union threshold (AIoU) was proposed based on the aspect-ratio information of ships to raise positive samples. To obtain the masks in the RB-Boxes, a new Mask-segmentation Head (MaskHead) with the twice sampling processes was explored. In experiments to evaluate the RB-Boxes, the accuracy of the RB-Boxes output from the Detection Head (DetHead) of SRNet outperformed eight rotated object-detection networks. In experiments to evaluate the final segmentation masks, compared with several classic and state-of-the-art instance-segmentation networks, our proposed SRNet achieved more accurate ship instance masks in SAR images. The ablation studies demonstrated the effectiveness of the DAM in the SRNet and the AIoU for our network training.

In the field of autonomous driving and robotics, 3D object detection is a difficult, but important task. To improve the accuracy of detection, LiDAR, which collects the 3D point cloud of a scene, is updated constantly. But the density of the collected 3D points is low, and its distribution is unbalanced in the scene, which influences the accuracy of 3D object detectors in regards to object location and identification. Although corresponding high-resolution scene images from cameras can be used as supplemental information, poor fusion strategies can result in decreased accuracy compared with that of LiDAR-point-only detectors. Thus, to improve the detection performance for the classification, localization, and even boundary location of 3D objects, a two-stage detector with density-and-sparsity feature aggregation, called DASANet, is proposed in this paper. In the first stage, dense pseudo point clouds are generated with images from cameras and are used to obtain the initial proposals. In the second stage, two novel feature aggregation modules are designed to fuse LiDAR point information and pseudo point information, which refines the semantic and detailed representation of the feature maps. To supplement the semantic information of the highest-scale LiDAR features for object localization and classification, a triple differential information supplement (TDIS) module is presented to extract the LiDAR-pseudo differential features and enhance them in spatial, channel, and global dimensions. To increase the detailed information of the LiDAR features for object boundary location, a Siamese three-dimension coordinate attention (STCA) module is presented to extract stable LiDAR and pseudo point cloud features with a Siamese encoder and fuse these features using a three-dimension coordinate attention. Experiments using the KITTI Vision Benchmark Suite demonstrate the improved performance of our DASANet in regards to the localization and boundary location of objects. The ablation studies demonstrate the effectiveness of the TDIS and the STCA modules.