The infrared small and dim (S&D) target detection is one of the key techniques in the infrared search and tracking system. Since the local regions similar to infrared S&D targets spread over the whole background, exploring the correlation amongst image features in large-range dependencies to mine the difference between the target and background is crucial for robust detection. However, existing deep learning-based methods are limited by the locality of convolutional neural networks, which impairs the ability to capture large-range dependencies. Additionally, the S&D appearance of the infrared target makes the detection model highly possible to miss detection. To this end, we propose a robust and general infrared S&D target detection method with the transformer. We adopt the self-attention mechanism of the transformer to learn the correlation of image features in a larger range. Moreover, we design a feature enhancement module to learn discriminative features of S&D targets to avoid miss-detections. After that, to avoid the loss of the target information, we adopt a decoder with the U-Net-like skip connection operation to contain more information of S&D targets. Finally, we get the detection result by a segmentation head. Extensive experiments on two public datasets show the obvious superiority of the proposed method over state-of-the-art methods, and the proposed method has a stronger generalization ability and better noise tolerance.

Drone-view Object Detection (DOD) is a meaningful but challenging task. It hits a bottleneck due to two main reasons: (1) The high proportion of difficult objects ( e.g ., small objects, occluded objects, etc .) makes the detection performance unsatisfactory. (2) The unevenly distributed objects make detection inefficient. These two factors also lead to a phenomenon, obeying the Pareto principle, that some challenging regions occupying a low area proportion of the image have a significant impact on the final detection while the vanilla regions occupying the major area have a negligible impact due to the limited room for performance improvement. Motivated by the human visual system that naturally attempts to invest unequal energies in things of hierarchical difficulty for recognizing objects effectively, this paper presents a novel Pareto Refocusing Detection (PRDet) network that distinguishes the challenging regions from the vanilla regions under reverse-attention guidance and refocuses the challenging regions with the assistance of the region-specific context. Specifically, we first propose a Reverse-attention Exploration Module (REM) that excavates the potential position of difficult objects by suppressing the features which are salient to the commonly used detector. Then, we propose a Region-specific Context Learning Module (RCLM) that learns to generate specific contexts for strengthening the understanding of challenging regions. It is noteworthy that the specific context is not shared globally but unique for each challenging region with the exploration of spatial and appearance cues. Extensive experiments and comprehensive evaluations on the VisDrone2021-DET and UAVDT datasets demonstrate that the proposed PRDet can effectively improve the detection performance, especially for those difficult objects, outperforming state-of-the-art detectors. Furthermore, our method also achieves significant performance improvements on the DTU-Drone dataset for power inspection.

Infrared small target detection plays an important role in the infrared search and tracking applications. In recent years, deep learning techniques have been introduced to this task and achieved noteworthy effects. Following general object segmentation methods, existing deep learning methods usually process the image from the global view. However, the locality of small targets and extreme class-imbalance between the target and background pixels are not well-considered by these deep learning methods, which causes the low-efficiency on training and high-dependence on numerous data. A local patch network (LPNet) with global attention is proposed in this article to detect small targets by jointly considering the global and local properties of infrared small target images. From the global view, a supervised attention module trained by the small target spread map is proposed to suppress most background pixels irrelevant with small target features. From the local view, local patches are split from global features and share the same convolution weights with each other in an LPNet. By leveraging both the global and local properties, the data-driven framework proposed in this article has the ability of fusing multiscale features for small target detection. Extensive experiments on synthetic and real datasets show that the proposed method achieves the state-of-the-art performance in comparison with both traditional and deep learning methods.

Aerial object detection, as object detection in aerial images captured from an overhead perspective, has been widely applied in urban management, industrial inspection, and other aspects. However, the performance of existing aerial object detection algorithms is hindered by variations in object scales and orientations attributed to the aerial perspective. This survey presents a comprehensive review of recent advances in aerial object detection. We start with some basic concepts of aerial object detection and then summarize the five imbalance problems of aerial object detection, including scale imbalance, spatial imbalance, objective imbalance, semantic imbalance, and class imbalance. Moreover, we classify and analyze relevant methods and especially introduce the applications of aerial object detection in practical scenarios. Finally, the performance evaluation is presented on two popular aerial object detection datasets VisDrone-DET and DOTA, and we discuss several future directions that could facilitate the development of aerial object detection.

Precise segmentation of teeth from intra-oral scanner images is an essential task in computer-aided orthodontic surgical planning. The state-of-the-art deep learning-based methods often simply concatenate the raw geometric attributes (i.e., coordinates and normal vectors) of mesh cells to train a single-stream network for automatic intra-oral scanner image segmentation. However, since different raw attributes reveal completely different geometric information, the naive concatenation of different raw attributes at the (low-level) input stage may bring unnecessary confusion in describing and differentiating between mesh cells, thus hampering the learning of high-level geometric representations for the segmentation task. To address this issue, we design a two-stream graph convolutional network (i.e., TSGCN), which can effectively handle inter-view confusion between different raw attributes to more effectively fuse their complementary information and learn discriminative multi-view geometric representations. Specifically, our TSGCN adopts two input-specific graph-learning streams to extract complementary high-level geometric representations from coordinates and normal vectors, respectively. Then, these single-view representations are further fused by a self-attention module to adaptively balance the contributions of different views in learning more discriminative multi-view representations for accurate and fully automatic tooth segmentation. We have evaluated our TSGCN on a real-patient dataset of dental (mesh) models acquired by 3D intraoral scanners. Experimental results show that our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.