Open Access

ARTICLE

CAW-YOLO: Cross-Layer Fusion and Weighted Receptive Field-Based YOLO for Small Object Detection in Remote Sensing

by Weiya Shi^1,*, Shaowen Zhang², Shiqiang Zhang²

1 College of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou, 450001, China
2 College of Information Science and Engineering, Henan University of Technology, Zhengzhou, 450001, China

* Corresponding Author: Weiya Shi. Email:

(This article belongs to the Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)

Computer Modeling in Engineering & Sciences 2024, 139(3), 3209-3231. https://doi.org/10.32604/cmes.2023.044863

Received 10 August 2023; Accepted 04 December 2023; Issue published 11 March 2024

Abstract

In recent years, there has been extensive research on object detection methods applied to optical remote sensing images utilizing convolutional neural networks. Despite these efforts, the detection of small objects in remote sensing remains a formidable challenge. The deep network structure will bring about the loss of object features, resulting in the loss of object features and the near elimination of some subtle features associated with small objects in deep layers. Additionally, the features of small objects are susceptible to interference from background features contained within the image, leading to a decline in detection accuracy. Moreover, the sensitivity of small objects to the bounding box perturbation further increases the detection difficulty. In this paper, we introduce a novel approach, Cross-Layer Fusion and Weighted Receptive Field-based YOLO (CAW-YOLO), specifically designed for small object detection in remote sensing. To address feature loss in deep layers, we have devised a cross-layer attention fusion module. Background noise is effectively filtered through the incorporation of Bi-Level Routing Attention (BRA). To enhance the model’s capacity to perceive multi-scale objects, particularly small-scale objects, we introduce a weighted multi-receptive field atrous spatial pyramid pooling module. Furthermore, we mitigate the sensitivity arising from bounding box perturbation by incorporating the joint Normalized Wasserstein Distance (NWD) and Efficient Intersection over Union (EIoU) losses. The efficacy of the proposed model in detecting small objects in remote sensing has been validated through experiments conducted on three publicly available datasets. The experimental results unequivocally demonstrate the model’s pronounced advantages in small object detection for remote sensing, surpassing the performance of current mainstream models.

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Keywords

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