Aircraft Detection in High Spatial Resolution Remote Sensing Images Combining Multi-Angle Features Driven and Majority Voting CNN

Abstract

Aircraft is a means of transportation and weaponry, which is crucial for civil and military fields to detect from remote sensing images. However, detecting aircraft effectively is still a problem due to the diversity of the pose, size, and position of the aircraft and the variety of objects in the image. At present, the target detection methods based on convolutional neural networks (CNNs) lack the sufficient extraction of remote sensing image information and the post-processing of detection results, which results in a high missed detection rate and false alarm rate when facing complex and dense targets. Aiming at the above questions, we proposed a target detection model based on Faster R-CNN, which combines multi-angle features driven and majority voting strategy. Specifically, we designed a multi-angle transformation module to transform the input image to realize the multi-angle feature extraction of the targets in the image. In addition, we added a majority voting mechanism at the end of the model to deal with the results of the multi-angle feature extraction. The average precision (AP) of this method reaches 94.82% and 95.25% on the public and private datasets, respectively, which are 6.81% and 8.98% higher than that of the Faster R-CNN. The experimental results show that the method can detect aircraft effectively, obtaining better performance than mature target detection networks.

A Survey of Small Object Detection Based on Deep Learning in Aerial Images

“…Problem solved Optimization Strategies single-stage detector [41] Boundary-arbitrary discontinuity FAB+DRBs+CRB DA-Net [42] Boundary-arbitrary discontinuity RFS+RFA MGAR [43] Vague angle representation CAC+FAR+IFL OrtDet [44] Angular periodicity Mean rotational accuracy (mRP) AProNet [45] Angular periodicity Axis-based Angle Learning Arbitrary orientation regression [46] Arbitrary angle Adaptive target orientation regression CFC-Net [47] Arbitrary angle Rotation anchor refinement module Angle encoding mechanism [48] Arbitrary angle Aspect ratio-based bidirectional coding label RH-RCNN [49] Arbitrary angle Distinguish tilted targets AOPDet [50] Rotation object representation Non-sequential angular representation ACE [51] Rotation object representation Directed quadrilateral box Faster R-CNN-based [52] Rotated Region Proposal Majority voting strategy RiDOP [53] Rotated Region Proposal Sliding only two vertices R-RCNN [54] Rotated Region Proposal Directional RoI pooling operation Point RCNN [55] Rotated Region Proposal PointRPN module generates RRoI New anchor-free detector [56] Arbitrary angle Center Boundary Dual-Attention (CBDA) AOPG [57] Arbitrary angle Generates orientation boxes in an anchor-free manner AOPG+FRIoU [58] Arbitrary angle Focal Rotated Intersection over Union(FRIoU) R2YOLOX [59] Arbitrary angle Refined Rotation Module (RRM) DARDet [60] Arbitrary angle ACM+PIoU ADT-Det [61] Inadequate expression of features Feature Pyramid Transformer (FPT) AFA-FPN [62] Inadequate expression of features Employs RROI to rotate the horizontal frames RINet [63] Inadequate expression of features Flexible multi-branch online detector improvement FoRDet [64] Inadequate expression of features Foreground Relationship module (FRL)…”

“…One such approach involves incorporating angle information of the object into the region proposal. For instance,a Faster R-CNN-based target detection model is proposed in [52] that combines multi-angle feature driving with a majority voting strategy.In another approach, a novel, lightweight rotated region proposal network is proposed in RiDOP [53] that generates arbitrarily-oriented recommendations by sliding only two vertices on adjacent edges and uses a simple and efficient representation to describe oriented objects. RH-RCNN [49] is proposed for target detection in arbitrary directions.…”

Cloud detection algorithm based on point by point refinement

“…Li et al [4] proposed a cloud detection method based on generative adversarial networks, using discriminators to distinguish between cloudy and cloud-free images and segmentation networks to detect the differences between cloudy and cloud-free images. Ji et al [5] proposed an F-CNN remote sensing image cloud detection method based on a fully convolutional neural network model, which can achieve cloud segmentation in large scale, high resolution, multi-channel remote sensing images, but the method is difficult to distinguish between clouds and snow. Dehnavi et al [6] proposed a cloud detection method based on stereo analysis output, which identifies and detects multicloud pixels with different cloud amounts through scatter plots, but the method covers complex regions with cloud It is difficult to determine a suitable threshold for accurate detection.…”

Aircraft Rotation Detection in Remote Sensing Image Based on Multi-Feature Fusion and Rotation-Aware Anchor

“…With the development of sensor and aerospace technology, large volume and high-resolution remote sensing images have accumulated. Object detection in remote sensing images has become a research hotspot [2][3][4]. Currently, aircraft are a common means for transportation and warfare.…”