Axis Learning for Orientated Objects Detection in Aerial Images

Xiao, Zhifeng; Qian, Linjun; Shao, Wei; Tan, Xiaowei; Wang, Kai

doi:10.3390/rs12060908

Cited by 62 publications

(21 citation statements)

References 33 publications

(94 reference statements)

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“…In the inference process, we divide test images into 600 with the overlap of 200, referring to R3Det [3]. A 2 S-Det has better performance than most detectors in Table 1, including one-stage detectors [3,12,19,20,[29][30][31] Figure 6.…”

Section: Resultsmentioning

confidence: 99%

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A2S-Det: Efficiency Anchor Matching in Aerial Image Oriented Object Detection

et al. 2020

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Object detection is a challenging task in aerial images, where many objects have large aspect ratios and are densely arranged. Most anchor-based rotating detectors assign anchors for ground-truth objects by a fixed restriction of the rotation Intersection-over-Unit (IoU) between anchors and objects, which directly follow horizontal detectors. Due to many directional objects with a large aspect ratio, the object-anchor IoU is heavily influenced by the angle, which may cause few anchors assigned for some ground-truth objects. In this study, we propose an anchor selection method based on sample balance assigning anchors adaptively, which we name the Self-Adaptive Anchor Selection (A2S-Det) method. For each ground-truth object, A2S-Det selects a set of candidate anchors by horizontal IoU. Then, an adaptive threshold module is adopted on the set of candidate anchors, which calculates a boundary of these candidate anchors aiming to keep a balance between positive and negative anchors. In addition, we propose a coordinate regression of relative reference (CR3) module to precisely regress the rotating bounding box. We test our method on a public aerial image dataset, and prove better performance than many other one-stage detectors and two-stage detectors, achieving the mAP of 70.64. An efficiency anchor matching method helps the detector achieve better performance for objects with large aspect ratios.

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Section: Resultsmentioning

confidence: 99%

“…Many rotation detectors aim at how to define rotation boxes and how to define samples. For instance, Axis-learning [19] predicts the axis of rotation objects based on the idea of anchor free, which has good performance and inference fast. O2-DNet [20] define boxes as two middle lines and the intersection point of middle lines.…”

Section: Related Workmentioning

confidence: 99%

A2S-Det: Efficiency Anchor Matching in Aerial Image Oriented Object Detection

et al. 2020

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“…Similarly, TOSO [41] designed a robust Student's T distribution-aided one-stage orientation detector to address orientation target detection in ORSIs. Xiao et al [42] proposed to detect the arbitrarily oriented objects in ORSIs by predicting the axis of the object at the pixel level of feature maps. Different from the aforementioned method that directly regresses the scales or the angle of the AOBB, our proposed MSO 2 -Det quantizes the boundless regression spaces by a classification-to-regression multi-sector strategy, which accelerates the convergence of the network and obtains more accurate localization of AOBBs in ORSIs.…”

Section: Anchor-free Object Detection Methodsmentioning

confidence: 99%

Multi-Sector Oriented Object Detector for Accurate Localization in Optical Remote Sensing Images

et al. 2021

Remote Sensing

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Oriented object detection in optical remote sensing images (ORSIs) is a challenging task since the targets in ORSIs are displayed in an arbitrarily oriented manner and on small scales, and are densely packed. Current state-of-the-art oriented object detection models used in ORSIs primarily evolved from anchor-based and direct regression-based detection paradigms. Nevertheless, they still encounter a design difficulty from handcrafted anchor definitions and learning complexities in direct localization regression. To tackle these issues, in this paper, we proposed a novel multi-sector oriented object detection framework called MSO2-Det, which quantizes the scales and orientation prediction of targets in ORSIs via an anchor-free classification-to-regression approach. Specifically, we first represented the arbitrarily oriented bounding box as four scale offsets and angles in four quadrant sectors of the corresponding Cartesian coordinate system. Then, we divided the scales and angle space into multiple discrete sectors and obtained more accurate localization information by a coarse-granularity classification to fine-grained regression strategy. In addition, to decrease the angular-sector classification loss and accelerate the network’s convergence, we designed a smooth angular-sector label (SASL) that smoothly distributes label values with a definite tolerance radius. Finally, we proposed a localization-aided detection score (LADS) to better represent the confidence of a detected box by combining the category-classification score and the sector-selection score. The proposed MSO2-Det achieves state-of-the-art results on three widely used benchmarks, including the DOTA, HRSC2016, and UCAS-AOD data sets.

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“…For example, Wei et al [51] proposed O 2 DNet, which encodes oriented objects as pairs of middle lines. Other models [52][53][54] have also been adopted using anchor-free strategies.…”

Section: Object Detection In Remote Sensing Imagesmentioning

confidence: 99%

LR-TSDet: Towards Tiny Ship Detection in Low-Resolution Remote Sensing Images

Pan

Lei

et al. 2021

Remote Sensing

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Recently, deep learning-based methods have made great improvements in object detection in remote sensing images (RSIs). However, detecting tiny objects in low-resolution images is still challenging. The features of these objects are not distinguishable enough due to their tiny size and confusing backgrounds and can be easily lost as the network deepens or downsamples. To address these issues, we propose an effective Tiny Ship Detector for Low-Resolution RSIs, abbreviated as LR-TSDet, consisting of three key components: a filtered feature aggregation (FFA) module, a hierarchical-atrous spatial pyramid (HASP) module, and an IoU-Joint loss. The FFA module captures long-range dependencies by calculating the similarity matrix so as to strengthen the responses of instances. The HASP module obtains deep semantic information while maintaining the resolution of feature maps by aggregating four parallel hierarchical-atrous convolution blocks of different dilation rates. The IoU-Joint loss is proposed to alleviate the inconsistency between classification and regression tasks, and guides the network to focus on samples that have both high localization accuracy and high confidence. Furthermore, we introduce a new dataset called GF1-LRSD collected from the Gaofen–1 satellite for tiny ship detection in low-resolution RSIs. The resolution of images is 16m and the mean size of objects is about 10.9 pixels, which are much smaller than public RSI datasets. Extensive experiments on GF1-LRSD and DOTA-Ship show that our method outperforms several competitors, proving its effectiveness and generality.

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Axis Learning for Orientated Objects Detection in Aerial Images

Cited by 62 publications

References 33 publications

A2S-Det: Efficiency Anchor Matching in Aerial Image Oriented Object Detection

A2S-Det: Efficiency Anchor Matching in Aerial Image Oriented Object Detection

Multi-Sector Oriented Object Detector for Accurate Localization in Optical Remote Sensing Images

LR-TSDet: Towards Tiny Ship Detection in Low-Resolution Remote Sensing Images

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