PAG-YOLO: A Portable Attention-Guided YOLO Network for Small Ship Detection

Hu, Jianming; Zhi, Xu; Shi, Tianjun; Zhang, Wei; Cui, Yinan; Zhao, Shenggang

doi:10.3390/rs13163059

Cited by 52 publications

(28 citation statements)

References 29 publications

(35 reference statements)

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“…Tan et al [ 11 ] proposed a simple and efficient bidirectional feature pyramid network named BiFPN, which introduced learnable weights to learn the importance of different input features. Hu et al proposed PAG-YOLO [ 25 ] with attention mechanisms in spatial and channel dimensions to adaptively assign the importance of features at different scales.…”

Section: Related Workmentioning

confidence: 99%

CAA-YOLO: Combined-Attention-Augmented YOLO for Infrared Ocean Ships Detection

Yuan

Qian

et al. 2022

Sensors

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Infrared ocean ships detection still faces great challenges due to the low signal-to-noise ratio and low spatial resolution resulting in a severe lack of texture details for small infrared targets, as well as the distribution of the extremely multiscale ships. In this paper, we propose a CAA-YOLO to alleviate the problems. In this study, to highlight and preserve features of small targets, we apply a high-resolution feature layer (P2) to better use shallow details and the location information. In order to suppress the shallow noise of the P2 layer and further enhance the feature extraction capability, we introduce a TA module into the backbone. Moreover, we design a new feature fusion method to capture the long-range contextual information of small targets and propose a combined attention mechanism to enhance the ability of the feature fusion while suppressing the noise interference caused by the shallow feature layers. We conduct a detailed study of the algorithm based on a marine infrared dataset to verify the effectiveness of our algorithm, in which the AP and AR of small targets increase by 5.63% and 9.01%, respectively, and the mAP increases by 3.4% compared to that of YOLOv5.

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Section: Related Workmentioning

confidence: 99%

CAA-YOLO: Combined-Attention-Augmented YOLO for Infrared Ocean Ships Detection

Yuan

Qian

et al. 2022

Sensors

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“…Furthermore, the attention module was applied to the CNN model which uses time-series data to estimate the blood pressure [36] and classifies the sleep stage in [37]. Many studies attempted to solve diverse problems on remote sensing image data such as classifications [38,39], ship detection [40,41], and semantic segmentation [42]. Ma et al [39] implemented the channel and spatial attention module and integrated it into CNN architecture for the classification of the remote sensing scene images.…”

Section: Attention Mechanismmentioning

confidence: 99%

Weakly Supervised Learning for Transmission Line Detection Using Unpaired Image-to-Image Translation

Choi

Lee

2022

Remote Sensing

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To achieve full autonomy of unmanned aerial vehicles (UAVs), obstacle detection and avoidance are indispensable parts of visual recognition systems. In particular, detecting transmission lines is an important topic due to the potential risk of accidents while operating at low altitude. Even though many studies have been conducted to detect transmission lines, there still remains many challenges due to their thin shapes in diverse backgrounds. Moreover, most previous methods require a significant level of human involvement to generate pixel-level ground truth data. In this paper, we propose a transmission line detection algorithm based on weakly supervised learning and unpaired image-to-image translation. The proposed algorithm only requires image-level labels, and a novel attention module, which is called parallel dilated attention (PDA), improves the detection accuracy by recalibrating channel importance based on the information from various receptive fields. Finally, we construct a refinement network based on unpaired image-to-image translation in order that the prediction map is guided to detect line-shaped objects. The proposed algorithm outperforms the state-of-the-art method by 2.74% in terms of F1-score, and experimental results demonstrate that the proposed method is effective for detecting transmission lines in both quantitative and qualitative aspects.

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“…In object detection based on deep learning for remote sensing images, Li et al [29] used the attention mechanism and Mask R-CNN, a convolutional neural network, to design an improved top-down FPN, which improved the detection precision of small objects with complex backgrounds for remote sensing images. Based on the YOLO network model [30], Hu et al [31] proposed a more advanced small marine vessel object detection method using the attention mechanism of spatial and channel information. Squeeze-and-excitation networks [32] are based on the principle of the attention mechanism, automatically obtaining the importance of each feature channel.…”

Section: Proposed Feature Extraction Networkmentioning

confidence: 99%

Improved Method to Detect the Tailings Ponds from Multispectral Remote Sensing Images Based on Faster R-CNN and Transfer Learning

Yan

Zhang

et al. 2021

Remote Sensing

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The breaching of tailings pond dams may lead to casualties and environmental pollution; therefore, timely and accurate monitoring is an essential aspect of managing such structures and preventing accidents. Remote sensing technology is suitable for the regular extraction and monitoring of tailings pond information. However, traditional remote sensing is inefficient and unsuitable for the frequent extraction of large volumes of highly precise information. Object detection, based on deep learning, provides a solution to this problem. Most remote sensing imagery applications for tailings pond object detection using deep learning are based on computer vision, utilizing the true-color triple-band data of high spatial resolution imagery for information extraction. The advantage of remote sensing image data is their greater number of spectral bands (more than three), providing more abundant spectral information. There is a lack of research on fully harnessing multispectral band information to improve the detection precision of tailings ponds. Accordingly, using a sample dataset of tailings pond satellite images from the Gaofen-1 high-resolution Earth observation satellite, we improved the Faster R-CNN deep learning object detection model by increasing the inputs from three true-color bands to four multispectral bands. Moreover, we used the attention mechanism to recalibrate the input contributions. Subsequently, we used a step-by-step transfer learning method to improve and gradually train our model. The improved model could fully utilize the near-infrared (NIR) band information of the images to improve the precision of tailings pond detection. Compared with that of the three true-color band input models, the tailings pond detection average precision (AP) and recall notably improved in our model, with the AP increasing from 82.3% to 85.9% and recall increasing from 65.4% to 71.9%. This research could serve as a reference for using multispectral band information from remote sensing images in the construction and application of deep learning models.

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PAG-YOLO: A Portable Attention-Guided YOLO Network for Small Ship Detection

Cited by 52 publications

References 29 publications

CAA-YOLO: Combined-Attention-Augmented YOLO for Infrared Ocean Ships Detection

CAA-YOLO: Combined-Attention-Augmented YOLO for Infrared Ocean Ships Detection

Weakly Supervised Learning for Transmission Line Detection Using Unpaired Image-to-Image Translation

Improved Method to Detect the Tailings Ponds from Multispectral Remote Sensing Images Based on Faster R-CNN and Transfer Learning

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