Remote Sensing Image Target Detection Method Based on Refined Feature Extraction

Abstract

To address the challenges posed by the large scale and dense distribution of small targets in remote sensing images, as well as the issues of missed detection and false detection, this paper proposes a one-stage target detection algorithm, DCN-YOLO, based on refined feature extraction techniques. First, we introduce DCNv2 and a residual structure to reconstruct a new backbone network, which enhances the extraction of shallow feature information and improves the network’s accuracy. Then, a novel feature fusion module is employed in the neck network to adaptively adjust the fusion weight for integrating texture information from shallow features with deep semantic information. This targeted approach effectively suppresses noise caused by extracting shallow features and enhances the representation of key features. Moreover, the normalized Gaussian Wasserstein distance loss, replacing Intersection over Union (IoU), is used as the regression loss function in the model, to enhance the detection capability of multi-scale targets. Finally, comparing our evaluations against recent advanced methods such as YOLOv7 and YOLOv6 demonstrates the effectiveness of the proposed approach, which achieves an average accuracy of 20.1% for small targets on the DOTAv1.0 dataset and 29.0% on the DIOR dataset.

A bearing surface defect detection method based on multi-attention mechanism Yolov8

“…This weakens the model's ability to recognize objects with significant geometric deformations. To alleviate this issue, this paper introduces deformable convolution with a modulation mechanism (DCNv2) [20] in the neck network, constructing a deep deformable feature network (C2f_DCN) that adequately integrates multi-scale information to enhance the detection accuracy of the multi-scale target model. Deformable convolution has convolution kernels that are not fixed N * N grids but rather samples with non-standard shapes.…”

DCEF2-YOLO: Aerial Detection YOLO with Deformable Convolution–Efficient Feature Fusion for Small Target Detection

“…The proposed model was evaluated on the DOTA-v1.0 (HBB) dataset. For fair verification, Table 6 shows a comparison using the same settings as Tian et al [39]. The DOTA dataset is utilized for training by dividing it into patches due to its irregular and very large image size.…”

Enhanced YOLOv8 with BiFPN-SimAM for Precise Defect Detection in Miniature Capacitors

“…Increasing complexity and the miniaturization trend in electronic products have brought significant challenges to quality control, especially in detecting minute defects in capacitors. Traditional methods, such as microscopic inspection and electrical testing, are becoming less effective in this evolving context [13]. This has led to the adoption of automated visual inspection methods using deep learning and convolutional neural networks (CNNs).…”

“…This is essential in real-world situations where sample variability and the effects of outliers are common. This enhancement was achieved via a refined feature layer hierarchy, which facilitates more effective feature transfer and integration across various scales, thus boosting the accuracy and dependability of defect detection [13,14]. Moreover, the incorporation of the WISE-IOU (WIoU) loss function significantly bolstered the model's generalization capabilities and robustness [12].…”