Orientation guided anchoring for geospatial object detection from remote sensing imagery

Yu, Yongtao; Guan, Haiyan; Li, Dilong; Gu, Tiannan; Tang, E.; Li, Aixia

doi:10.1016/j.isprsjprs.2019.12.001

Cited by 33 publications

(11 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [25], angle information was used together with some orientation parameters to make anchor better fit ship targets and thereby enable significant improvement in detection accuracy. Subsequently, various improved algorithms were proposed to address the problems including insufficient positive samples, feature misalignment, and inconsistency between classification and regression due to the introduction of rotating frames [26][27][28][29]. With the continuous improvements, today's deep learning-based ship detection algorithms can meet the required levels of accuracy and efficiency for civilian applications.…”

Section: Ship Detection Methodsmentioning

confidence: 99%

DD-Net: A Dual Detector Network for Multilevel Object Detection in Remote-Sensing Images

Zhang

Wang

2022

Journal of Sensors

View full text Add to dashboard Cite

With the recent development of deep convolutional neural network (CNN), remote sensing for ship detection methods has achieved enormous progress. However, current methods focus on the whole ships and fail on the component’s detection of a ship. To detect ships from remote-sensing images in a more refined way, we employ the inherent relationship between ships and their critical parts to establish a multilevel structure and propose a novel framework to improve the performance in identifying the multilevel objects. Our framework, named the dual detector network (DD-Net), consists of two carefully designed detectors, one for ships (the ship detector) and the other for their critical parts (the critical part detector), for detecting the critical parts in a coarse-to-fine manner. The ship detector offers detection results of the ship, based on which the critical part detector detects small critical parts inside each ship region. The framework is trained in an end-to-end way by optimizing the multitask loss. Due to the lack of publicly available datasets for critical part detection, we build a new dataset named RS-Ship with 1015 remote-sensing images and 2856 annotations. Experiments on the HRSC2016 dataset and the RS-Ship dataset show that our method performs well in the detection of ships and critical parts.

show abstract

Section: Ship Detection Methodsmentioning

confidence: 99%

DD-Net: A Dual Detector Network for Multilevel Object Detection in Remote-Sensing Images

Zhang

Wang

2022

Journal of Sensors

View full text Add to dashboard Cite

show abstract

“…One of the two subnetworks is responsible for training and generating anchors of appropriate size in the anchor generation stage. The other is responsible for counting the occurrence probability of the object, avoiding invalid anchors appearing in the background [ 36 ]. Tian et al used the attention mechanism to produce adaptive anchors to improve object detection in remote sensing images [ 37 ].…”

Section: Related Workmentioning

confidence: 99%

Research on Object Detection of PCB Assembly Scene Based on Effective Receptive Field Anchor Allocation

Chen

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Vision-based object detection of PCB (printed circuit board) assembly scenes is essential in accelerating the intelligent production of electronic products. In particular, it is necessary to improve the detection accuracy as much as possible to ensure the quality of assembly products. However, the lack of object detection datasets in PCB assembly scenes is the key to restricting intellectual PCB assembly research development. As an excellent representative of the one-stage object detection model, YOLOv3 (you only look once version 3) mainly relies on placing predefined anchors on the three feature pyramid layers and realizes recognition and positioning using regression. However, the number of anchors distributed in each grid cell of different scale feature layers is usually the same. The ERF (effective receptive field) corresponding to the grid cell at different locations varies. The contradiction between the uniform distribution of fixed-size anchors and the ERF size range in different feature layers will reduce the effectiveness of object detection. Few people use ERF as a standard for assigning anchors to improve detection accuracy. To address this issue, firstly, we constructed a PCB assembly scene object detection dataset, which includes 21 classes of detection objects in three scenes before assembly, during assembly, and after assembly. Secondly, we performed a refined ERF analysis on each grid of the three output layers of YOLOv3, determined the ERF range of each layer, and proposed an anchor allocation rule based on the ERF. Finally, for the small and difficult-to-detect TH (through-holes), we increased the context information and designed improved-ASPP (Atrous spatial pyramid pooling) and channel attention joint module. Through a series of experiments on the object detection dataset of the PCB assembly scene, we found that under the framework of YOLOv3, anchor allocation based on ERF can increase mAP (mean average precision) from 79.32% to 89.86%. At the same time, our proposed method is superior to Faster R-CNN (region convolution neural network), SSD (single shot multibox detector), and YOLOv4 (you only look once version 4) in the balance of high detection accuracy and low computational complexity.

show abstract

“…Some work directly used additional network modules such as oriented proposal boxes to achieve object detection [93], [94], or upgraded the general convolutional filter to a directional channel filter to achieve rotation invariant of texture [95]. The region proposal network (RPN) [96]- [98] added to the anchor boxed with multiple angles in order to cover the oriented object. Additionally, inspired by text detection methods [99]- [102], Xia et al [103] designed a direction insensitive FR-O network by adding a direction box detection sub-network to Faster RCNN.…”

Section: Object Detection On Direction Diversitymentioning

confidence: 99%

Deep Learning for UAV-based Object Detection and Tracking: A Survey

Wu,

Li,

Hong

et al. 2021

Preprint

View full text Add to dashboard Cite

This is the pre-acceptance version, to read the final version please go to IEEE Geoscience and Remote Sensing Magazine on IEEE Xplore. Owing to effective and flexible data acquisition, unmanned aerial vehicle (UAV) has recently become a hotspot across the fields of computer vision (CV) and remote sensing (RS). Inspired by recent success of deep learning (DL), many advanced object detection and tracking approaches have been widely applied to various UAV-related tasks, such as environmental monitoring, precision agriculture, traffic management. This paper provides a comprehensive survey on the research progress and prospects of DL-based UAV object detection and tracking methods. More specifically, we first outline the challenges, statistics of existing methods, and provide solutions from the perspectives of DL-based models in three research topics: object detection from the image, object detection from the video, and object tracking from the video. Open datasets related to UAV-dominated object detection and tracking are exhausted, and four benchmark datasets are employed for performance evaluation using some state-of-the-art methods. Finally, prospects and considerations for the future work are discussed and summarized. It is expected that this survey can facilitate those researchers who come from remote sensing field with an overview of DL-based UAV object detection and tracking methods, along with some thoughts on their further developments.

show abstract

Orientation guided anchoring for geospatial object detection from remote sensing imagery

Cited by 33 publications

References 66 publications

DD-Net: A Dual Detector Network for Multilevel Object Detection in Remote-Sensing Images

DD-Net: A Dual Detector Network for Multilevel Object Detection in Remote-Sensing Images

Research on Object Detection of PCB Assembly Scene Based on Effective Receptive Field Anchor Allocation

Deep Learning for UAV-based Object Detection and Tracking: A Survey

Contact Info

Product

Resources

About