Oriented Object Detection in Aerial Images with Box Boundary-Aware Vectors

Yi, Jingru; Wu, Pengxiang; Liu, Bo; Huang, Qi‐Fang; Qu, Hui; Metaxas, Dimitris N.

doi:10.1109/wacv48630.2021.00220

Cited by 244 publications

(122 citation statements)

References 18 publications

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“…Finally, after a refined analysis of the ERF, whether the smallest bounding rectangle of the active area of the ERF can be used in object orientation detection is still a mystery. Fortunately, according to recent experience, the current research status of deep feature alignment [ 65 ], dynamic learnable anchors [ 66 ], and object orientation detection [ 67 ] can all bring inspiration to solve the above problems. Therefore, future research related to anchors and ERF will continue to be in-depth in adapting to different object detection tasks, improving detection results, and increasing detection speed.…”

Section: Methodsmentioning

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

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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.

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Section: Methodsmentioning

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

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“…4, with large aspect ratios of the OBB, the small angle prediction errors will cause a rapid drop For key point regression-based methods, similar problems exist. Yi et al [47] expressed the target OBB as a group of midpoints from four edges of the OBB. The four midpoints are distributed in the four quadrants of the Cartesian Coordinates, respectively.…”

Section: A the Boundary Discontinuity Problemmentioning

confidence: 99%

“…It represents the position and shape of the OBB by the center point, width, height and the rotation angle of the OBB. The detailed introduction of pointbased encoding scheme can be referred to [47]. This method represents OBB by the center point and four vectors pointing from the center point to the midpoint of four edges of the OBB.…”

Section: B Comparison With Different Obb Encoding Schemesmentioning

confidence: 99%

“…Key point regression-based methods represent OBB as key points. For example, Yi et al [47] denoted target OBBs with four vectors distributed in different quadrants of Cartesian coordinates. Xu et al [48] first predicted the horizontal enclosing rectangle of the target OBB, then regressed the distances between the vertices of the enclosing rectangle and the vertices of the OBB to position the OBB.…”

Section: Introductionmentioning

confidence: 99%

“…To deal with the problem, researchers propose different approaches. For example, Yi et al [47] and Xu et al [48] first set a fixed threshold manually for distinguishing HBB from OBB, and added a network branch to classify the target bounding box as HBB or OBB. Different regression rules were adopted for different kinds of target bounding boxes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Learning Polar Encodings for Arbitrary-Oriented Ship Detection in SAR Images

Gao

Wang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Common horizontal bounding box (HBB)-based methods are not capable of accurately locating slender ship targets with arbitrary orientations in synthetic aperture radar (SAR) images. Therefore, in recent years, methods based on oriented bounding box (OBB) have gradually received attention from researchers. However, most of the recently proposed deep learning-based methods for OBB detection encounter the boundary discontinuity problem in angle or key point regression. In order to alleviate this problem, researchers propose to introduce some manually set parameters or extra network branches for distinguishing the boundary cases, which make training more difficult and lead to performance degradation. In this paper, in order to solve the boundary discontinuity problem in OBB regression, we propose to detect SAR ships by learning polar encodings. The encoding scheme uses a group of vectors pointing from the center of the ship target to the boundary points to represent an OBB. The boundary discontinuity problem is avoided by training and inference directly according to the polar encodings. In addition, we propose an Intersect over Union (IOU) -weighted regression loss, which further guides the training of polar encodings through the IOU metric and improves the detection performance. Experiments on the Rotating SAR Ship Detection Dataset (RSSDD) show that the proposed method can achieve better detection performance over other comparison algorithms and other OBB encoding schemes, demonstrating the effectiveness of our method. Index Terms-synthetic aperture radar(SAR), ship detection, arbitrary-orientated, polar encodings.

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