Object Detection Based on Global-Local Saliency Constraint in Aerial Images

Li, Chengyuan; Luo, Bin; Hong, Hailong; Su, Xin; Wang, Yajun; Li, Jun; Wang, Chenjie; Zhang, Jing; Wei, Linhai

doi:10.3390/rs12091435

Cited by 36 publications

(16 citation statements)

References 113 publications

(127 reference statements)

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“…When the confidence score of a detection that is not supposed to detect anything is lower than the threshold, it is considered a true negative (TN). However, this is not of great significance in object detection [16].…”

Section: Evaluation Metricsmentioning

confidence: 97%

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Vision-Based Deep Learning Algorithm for Detecting Potholes

Niekerk¹,

Wilm²,

Mercorelli³

2021

Preprint

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Potholes on roads pose a major threat to motorists and autonomous vehicles. Driving over a pothole has the potential to cause serious damage to a vehicle, which in turn may result in fatal accidents. Currently, many pothole detection methods exist. However, these methods do not utilize deep learning techniques to detect a pothole in real-time, determine the location thereof and display its location on a map. The success of determining an effective pothole detection method, which includes the aforementioned deep learning techniques, is dependent on acquiring a large amount of data, including images of potholes. Once adequate data had been gathered, the images were processed and annotated. The next step was to determine which deep learning algorithms could be utilized. Three different models, including Faster R-CNN, SSD and YOLOv3 were trained on the custom dataset containing images of potholes to determine which network produces the best results for real-time detection. It was revealed that YOLOv3 produced the most accurate results and performed the best in real-time, with an average detection time of only 0.836s per image. The final results revealed that a real-time pothole detection system, integrated with a cloud and maps service, can be created to allow drivers to avoid potholes.

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Section: Evaluation Metricsmentioning

confidence: 97%

“…It is clear that these two boxes have different co-ordinates. The area of intersection is where the one box overlaps the other and the area of union is the total area covered by both bounding boxes [16].…”

Section: Evaluation Metricsmentioning

confidence: 99%

Vision-Based Deep Learning Algorithm for Detecting Potholes

Niekerk¹,

Wilm²,

Mercorelli³

2021

Preprint

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“…However, location-wise attention does not necessarily require known inputs, in this case, the model needs to deal with input items that are difficult to distinguish. Due to the characteristics and features of the RS images and targeted tasks, location-wise attention is commonly used for RS image processing [42,[46][47][48]. (iii) Input representations: there are single-input and multi-input attention models [49,50].…”

Section: Attention Mechanism In Deep Learningmentioning

confidence: 99%

“…(ii) Object detection: refers to the detection of different objects in an image. It is the second most popular task that is addressed using At-DL including general object/target detection from RS images [46,60,95] or detection of the specific objects and features such as buildings [74,96], ships [97,98], landslides [99], clouds [53,100], airports [101], roads [72] and trees [102].…”

Section: Overview Of the Reviewed Papersmentioning

confidence: 99%

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

et al. 2021

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Machine learning, particularly deep learning (DL), has become a central and state-of-the-art method for several computer vision applications and remote sensing (RS) image processing. Researchers are continually trying to improve the performance of the DL methods by developing new architectural designs of the networks and/or developing new techniques, such as attention mechanisms. Since the attention mechanism has been proposed, regardless of its type, it has been increasingly used for diverse RS applications to improve the performances of the existing DL methods. However, these methods are scattered over different studies impeding the selection and application of the feasible approaches. This study provides an overview of the developed attention mechanisms and how to integrate them with different deep learning neural network architectures. In addition, it aims to investigate the effect of the attention mechanism on deep learning-based RS image processing. We identified and analyzed the advances in the corresponding attention mechanism-based deep learning (At-DL) methods. A systematic literature review was performed to identify the trends in publications, publishers, improved DL methods, data types used, attention types used, overall accuracies achieved using At-DL methods, and extracted the current research directions, weaknesses, and open problems to provide insights and recommendations for future studies. For this, five main research questions were formulated to extract the required data and information from the literature. Furthermore, we categorized the papers regarding the addressed RS image processing tasks (e.g., image classification, object detection, and change detection) and discussed the results within each group. In total, 270 papers were retrieved, of which 176 papers were selected according to the defined exclusion criteria for further analysis and detailed review. The results reveal that most of the papers reported an increase in overall accuracy when using the attention mechanism within the DL methods for image classification, image segmentation, change detection, and object detection using remote sensing images.

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“…Owing to large size of images, which will lead to poor results if images are directly input for training because the pictures will be compressed seriously, so we cropped a series of 618×618 patches with a stride of 300 from the original images, among which those with no annotation will be eliminated. For the experiment on NWPU-VHR, following [43,44,45], 60% of the data is used for testing and 20% is used for training. Input images are resized to 800×800 pixels.…”

Section: A Dataset Settingmentioning

confidence: 99%

Exploiting Full-Scale Feature for Remote Sensing Object Detection Based on Refined Feature Mining and Adaptive Fusion

Wang

Rui

et al. 2021

IEEE Access

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Object detection for remote sensing images remains a challenging problem. In this paper, we proposed an effective remote sensing detection network (RS-Net) based on YOLOv4, which greatly solves the difficulties of remote sensing object detection. Center to our RS-Net is coarse-grained highlighting (CGH), fine-grained mining (FGM) and scale distillation (SD) modules. Through a fusion of original and complementary feature, CGH alleviates the dilemma of object semantic extraction caused by the variability of background information. Besides, FGM is designed to further enhance the feature extraction ability, which is implemented by prominent branch and ensemble branch in parallel. The former highlights the object semantics of being suppressed due to its high similarity with background. The latter mines object information as a whole to reduce the interference from noise. Finally, SD significantly improves the richness of small object information in shallow layer. Experiments conducted over DOTA and NWPU-VHR datasets confirm that the proposed RS-Net achieves competitive detection performance compared with state-of-the-art detectors.

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Object Detection Based on Global-Local Saliency Constraint in Aerial Images

Cited by 36 publications

References 113 publications

Vision-Based Deep Learning Algorithm for Detecting Potholes

Vision-Based Deep Learning Algorithm for Detecting Potholes

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

Exploiting Full-Scale Feature for Remote Sensing Object Detection Based on Refined Feature Mining and Adaptive Fusion

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