Oriented object detection is crucial for complex scenes such as aerial images and industrial inspection, providing precise delineation by minimizing background interference. Recently, the weakly-supervised detector paradigm H2RBox has demonstrated promise in learning rotated bounding box (RBox) from the more readily available horizontal bounding box (HBox), alleviating the scarcity and high cost of RBox annotations. However, these H2RBox-based methods have primarily focused on the gap in orientation information between HBox- and RBox-supervised approaches, overlooking the gap in training sample selection. In response, we propose the Adaptive Fine-grained Sample Mining (AFSM) strategy, which improves the selection of fine-grained training samples in HBox-supervised methods. AFSM assigns the best-matching prediction RBox to each ground truth (GT) HBox and selects positive samples based on these paired boxes. Furthermore, to effectively filter the best-matching prediction RBox for AFSM, we introduce the Prediction Rbox Assignment (PRA) scheme, employing Kullback-Leibler Divergence (KLD) as a localization quality metric. Additionally, we introduce an improved self-supervised branch loss (Lss) to address the symmetry of weakly-supervised branch prediction boxes. Incorporating these core components (AFSM, PRA, and Lss), we develop an end-to-end network architecture (BGHR) to further bridge the gap between HBox- and RBox-supervised oriented object detection. Extensive experiments on DOTA-v1.0 and DIOR-R demonstrate that BGHR achieves state-of-the-art performance compared to HBox-supervised methods without additional overhead. Even when benchmarked against fully supervised FCOS, our method still exhibits a slight performance advantage.

Semi-supervised arbitrary-oriented object detection (Semi-AOOD) has received increasing attention for its capability to improve detection performance by leveraging unlabeled data. Most existing works are based on the pseudo-label framework, which focuses on enhancing the consistency of the teacher-student network for better performance. However, they neglect the unique geometric characteristic of arbitrary-oriented objects during the feature extraction and overlook the potentially interfering information in pseudo-labels that may influence the optimization direction. In this paper, we propose a novel semi-supervised arbitrary-oriented object detection model called AODet (Adaptive Optimization Detector) to tackle these problems. First, we introduce an object adaptive strategy (OAS) to highlight the important regions while adaptively extracting the geometric features of arbitrary-oriented objects. Second, we develop an adaptive pseudo-label refinement strategy (APRS) to reduce the classification scores of low-confident categories in pseudo-labels. Extensive experiments on the DOTA dataset demonstrate the effectiveness of our AODet.

Smoke detection can warn fires and prevent them. Visual-based smoke detection has gained more attention. However, there is a lack of publicly available smoke datasets in this area. To fill this gap, in this paper, we build a smoke dataset based on various realistic environments that contains 24776 smoke RGB images. Furthermore, a Contextual Interaction Enhancement Network for smoke detection is presented. Re-parameterized large kernel convolutions are utilized in the backbone to increase the receptive field of smoke feature extraction. And a Contextual Interaction Enhancement Module (CIEM)-neck is proposed to get aggregated features for better feature assignment to the decoupled head. Experimental results show that our method has excellent performance, achieving state-of-the-art results on the proposed Smoke 24776 dataset at 83.8% precision, 77.3% [email protected] and 49.8% mAP. Additionally, even if it is specialized for smoke detection, our method has still competitive performance and generalization on famous MS-COCO with 66% [email protected] and 47.0% mAP. Dataset is available at https://github.com/linjiefengFutureMediaSZU/Smoke24776.

This study proposes a hybrid feature convolutional neural network (HFCNN) model for the complete description of three‐dimensional (3D) point cloud features. The HFCNN confers sensitivity to the local, global, and single‐point properties simultaneously by a feature vector space expansion. Wherein, a pointwise convolutional network sub‐model realises the extraction of the local features by using a pointwise convolutional operator to process point cloud data directly. To consider the global properties of the point cloud, a central‐point radiation model is constructed as an input of the feature layer in a non‐network form. Meanwhile, the single‐point behaviour is characterised by the solo point coordinate information in the network. Within the constructed solo‐local‐global feature space, i.e. the fusion of single point feature, local feature and global feature, the HFCNN model can handle 3D point cloud data with unstructured and unordered properties. The HFCNN can be directly applied to the point cloud classification and segmentation without the modification of the CNN structure and training procedure. The experimental results have shown the effectiveness of the proposed model in prediction of class labels and point‐by‐point labels.