MSB-FCN: Multi-Scale Bidirectional FCN for Object Skeleton Extraction

Edge-Supervised Linear Object Skeletonization for High-Speed Camera

This paper presents a high-speed skeletonization algorithm for detecting the skeletons of linear objects from their binary images. The primary objective of our research is to achieve rapid extraction of the skeletons from binary images while maintaining accuracy for high-speed cameras. The proposed algorithm uses edge supervision and a branch detector to efficiently search inside the object, avoiding unnecessary computation on irrelevant pixels outside the object. Additionally, our algorithm addresses the challenge of self-intersections in linear objects with a branch detection module, which detects existing intersections and initializes new searches on emerging branches when necessary. Experiments on various binary images, such as numbers, ropes, and iron wires, demonstrated the reliability, accuracy, and efficiency of our approach. We compared the performance of our method with existing skeletonization techniques, showing its superiority in terms of speed, especially for larger image sizes.

Swin-Transformer-Based YOLOv5 for Small-Object Detection in Remote Sensing Images

This study aimed to address the problems of low detection accuracy and inaccurate positioning of small-object detection in remote sensing images. An improved architecture based on the Swin Transformer and YOLOv5 is proposed. First, Complete-IOU (CIOU) was introduced to improve the K-means clustering algorithm, and then an anchor of appropriate size for the dataset was generated. Second, a modified CSPDarknet53 structure combined with Swin Transformer was proposed to retain sufficient global context information and extract more differentiated features through multi-head self-attention. Regarding the path-aggregation neck, a simple and efficient weighted bidirectional feature pyramid network was proposed for effective cross-scale feature fusion. In addition, extra prediction head and new feature fusion layers were added for small objects. Finally, Coordinate Attention (CA) was introduced to the YOLOv5 network to improve the accuracy of small-object features in remote sensing images. Moreover, the effectiveness of the proposed method was demonstrated by several kinds of experiments on the DOTA (Dataset for Object detection in Aerial images). The mean average precision on the DOTA dataset reached 74.7%. Compared with YOLOv5, the proposed method improved the mean average precision (mAP) by 8.9%, which can achieve a higher accuracy of small-object detection in remote sensing images.

ProMask: Probability mask representation for skeleton detection

Detecting object skeletons in natural images presents challenges due to varied object scales and complex backgrounds. The skeleton is a highly compressing shape representation, which can bring some essential advantages but cause difficulties in detection. This skeleton line occupies a small part of the image and is overly sensitive to spatial position. Inspired by these issues, we propose the ProMask, which is a novel skeleton detection model. The ProMask includes the probability mask representation and vector router. This skeleton probability mask describes the gradual formation process of skeleton points, which can achieve high detection performance and robustness. Moreover, the vector router module possesses two sets of orthogonal basis vectors in a two-dimensional space, which can dynamically adjust the predicted skeleton position. Experiments show that our approach realizes better performance, efficiency, and robustness than state-of-the-art methods. We consider that our proposed skeleton probability representation will serve as a standard configuration for future skeleton detection, since it is reasonable, simple, and very effective.

Research on Ground Object Classification Method of High Resolution Remote-Sensing Images Based on Improved DeeplabV3

Ground-object classification using remote-sensing images of high resolution is widely used in land planning, ecological monitoring, and resource protection. Traditional image segmentation technology has poor effect on complex scenes in high-resolution remote-sensing images. In the field of deep learning, some deep neural networks are being applied to high-resolution remote-sensing image segmentation. The DeeplabV3+ network is a deep neural network based on encoder-decoder architecture, which is commonly used to segment images with high precision. However, the segmentation accuracy of high-resolution remote-sensing images is poor, the number of network parameters is large, and the cost of training network is high. Therefore, this paper improves the DeeplabV3+ network. Firstly, MobileNetV2 network was used as the backbone feature-extraction network, and an attention-mechanism module was added after the feature-extraction module and the ASPP module to introduce focal loss balance. Our design has the following advantages: it enhances the ability of network to extract image features; it reduces network training costs; and it achieves better semantic segmentation accuracy. Experiments on high-resolution remote-sensing image datasets show that the mIou of the proposed method on WHDLD datasets is 64.76%, 4.24% higher than traditional DeeplabV3+ network mIou, and the mIou on CCF BDCI datasets is 64.58%. This is 5.35% higher than traditional DeeplabV3+ network mIou and outperforms traditional DeeplabV3+, U-NET, PSP-NET and MACU-net networks.