Improved loss and feature fusion for water surface object detection

Yunfeng Xu; Changming Zhu

doi:10.1117/12.2680986

8 June 2023 Improved loss and feature fusion for water surface object detection

Yunfeng Xu, Changming Zhu

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127072D (2023) https://doi.org/10.1117/12.2680986
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

Water surface object detection is one of the essential techniques for carrying out water-related tasks and assisting in the conduct of vessels. However, water surface object detection algorithms currently have low-precision problems detecting small and irregular objects, and difficult samples are hard to detect in harsh environments. To this end, an improved loss and enhancement feature fusion method is proposed to optimize the YOLOX object detection algorithm for water surface target detection. YOLOX, as an anchorless frame algorithm, has better detection capability for irregular objects. Improved loss improves the focus on difficult samples and treats positive samples asymmetrically based on EIoU loss. The Lite-RFP enhanced feature fusion mechanism enables the network to recursively pass contextual information recursively. It enables the shallow network to integrate deep semantic information better, improving the performance of small target detection and maintaining a lighter network structure. The experimental results show that the improved algorithm based on YOLOXs improves the mAP value by 4.11 percentage points compared to the original YOLOX-l. At the same time, a real-time detection speed of 56.534 FPS can be achieved, and the detection problems caused by different water surface environments are improved.

Citation Download Citation

Yunfeng Xu and Changming Zhu "Improved loss and feature fusion for water surface object detection", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127072D (8 June 2023); https://doi.org/10.1117/12.2680986

ACCESS THE FULL ARTICLE

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: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Object detection

Target detection

Detection and tracking algorithms

Feature fusion

Environmental sensing

Small targets

Water

Keywords/Phrases

Search In:

Publication Years