IECE - Institute of Emerging and Computer Engineers

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    • gaolongfei@st.btbu.edu.cn
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    Author 2
    Longfei Gao
    Beijing Technology and Business University
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    Free Access | Research Article | 27 May 2024 | Cited: 5
    YOLOv7-Bw: A Dense Small Object Efficient Detector Based on Remote Sensing Image
    by
    Xuebo Jin
    ,
    Anshuo Tong
    ,
    Xudong Ge
    ,
    Huijun Ma
    ,
    Jiaxi Li
    ,
    Heran Fu
    ,
    Longfei Gao
    IECE Transactions on Intelligent Systematics | Volume 1, Issue 1: 30-39, 2024 | DOI:10.62762/TIS.2024.137321
    Abstract
    In recent years, deep learning techniques have been increasingly applied to the detection of remote sensing images. However, the substantial size variation and dense distribution of objects in these images present significant challenges to detection algorithms. Current methods often suffer from low efficiency, missed detections, and inaccurate bounding boxes. To address these issues, this paper presents an improved YOLO algorithm, YOLOv7-bw, designed for efficient remote sensing image detection, thereby advancing object detection applications in the remote sensing industry. YOLOv7-bw enhances the original SPPCSPC pooling pyramid network by incorporating a Bi-level Routing Attention module, w... More >
    Graphical Abstract
    YOLOv7-Bw: A Dense Small Object Efficient Detector Based on Remote Sensing Image
    Free Access | Research Article | 15 May 2024 | Cited: 1
    Visual Feature Extraction and Tracking Method Based on Corner Flow Detection
    by
    Jiaxi Li
    ,
    Binbin Wang
    ,
    Huijun Ma
    ,
    Longfei Gao
    ,
    Heran Fu
    IECE Transactions on Intelligent Systematics | Volume 1, Issue 1: 3-9, 2024 | DOI:10.62762/TIS.2024.136895
    Abstract
    Frontend feature tracking based on vision is the process in which a robot captures images of its surrounding environment using a camera while in motion. Each frame of the image is then analyzed to extract feature points, which are subsequently matched between pairwise frames to estimate the robot’s pose changes by solving for the variations in these points. While feature matching methods that rely on descriptor-based approaches perform well in cases of significant lighting and texture variations, the addition of descriptors increases computational costs and introduces instability. Therefore, in this paper, a novel approach is proposed that combines sparse optical flow tracking with Shi-Tom... More >
    Graphical Abstract
    Visual Feature Extraction and Tracking Method Based on Corner Flow Detection