Mr. Huigang Zheng Profile

Huigang Zheng

SPIE Involvement:

Author

Publications (2)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 1 August 2023 Paper

An efficient harbor ship detection method based on deep learning in high-resolution remote sensing images

Zengcheng Yu, Huigang Zheng

Proceedings Volume 12754, 127541N (2023) https://doi.org/10.1117/12.2684515

KEYWORDS: Detection and tracking algorithms, Remote sensing, Target detection, Object detection, Head, Deep learning, Semantics, Feature extraction, Network architectures, Feature fusion

Read Abstract +

With the increasing prosperity of shipping, effective harbor management has become a requirement. High-resolution optical remote sensing images can effectively monitor and manage ships due to their rich spatial information, clear geometric information, and unique top-down view. However, harbor scenes usually have complex background information and densely distributed ship targets, these two factors increase the difficulty of the algorithm to detect ship targets in remote sensing harbor scenes. With respect to these two issues, this paper proposes a novel harbor ship detection method based on YOLOv6-s with finer-feature fusion which is called F-YOLOv6. First, Extract ship targets from complex ground information. By importing the backbone extraction network efficient re-parameterization (Efficient-Rep) of YOLOv6-s, its efficient extraction ability can help the algorithm to improve the detection accuracy. Second, to improve the detection performance of the algorithm for densely arranged ship targets, the finer pixel aggregation network (F-PAN) is constructed to obtain more ship object position information, and on this basis, it is integrated with the deep high-level semantic information to improve the robustness of the algorithm. Third, to further improve the timeliness of the algorithm, the detection efficiency of the algorithm is improved by deleting some redundant prediction headers. Finally, extensive experiments are carried out on the harbor ship detection (HSD) dataset, which is suitable for densely arranged ship detection, to verify the effectiveness of the proposed method. In addition, the method is verified by the DIOR public dataset, and its generalization is better than other benchmark algorithms.

Proceedings Article | 16 December 2022 Paper

Contrasting YOLO series and CenterNet detectors for poppy detection in different environments

Zengcheng Yu, Huigang Zheng

Proceedings Volume 12500, 125003A (2022) https://doi.org/10.1117/12.2662656

KEYWORDS: Detection and tracking algorithms, Unmanned aerial vehicles, Sensors, Environmental sensing, Head, Feature extraction, Image transmission, Neural networks, Boron, Transmittance

Read Abstract +

As the main drug original plant, the accurate identification of poppy has become the key to anti-drug work. Compared with other spatial data collection methods, unmanned aerial vehicle (UAV) image collection technology can monitor across regions in real time, and its flexibility can improve the efficiency of anti-drug work. However, the UAV art remains short of contrastive assessment of recent deep architectures for poppy objects in different environments UAV images. In addition, foggy weather is often present during the most detectable flowering period for poppies. The existence of fog has a certain impact on the anti-drug work that may be carried out at any time. At present, there is a lack of data samples of poppy pictures in foggy weather, so it is difficult to proceed on related research, to solve the aforementioned problems. This paper compares the detection and statistics of poppies in UAV images by state-of-the-art deep learning-based target detection algorithms in different weather environments. These algorithms include YOLO series and CenterNet. To this end, this paper collects and produces a poppy dataset of UAV images in two different weather environments. Through extensive experiments, the performance of state-of-the-art target detection algorithm to detect poppy under different weather conditions is evaluated.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years