Multiple-Oriented and Small Object Detection with Convolutional Neural Networks for Aerial Image

Abstract

Detecting objects in aerial images is a challenging task due to multiple orientations and relatively small size of the objects. Although many traditional detection models have demonstrated an acceptable performance by using the imagery pyramid and multiple templates in a sliding-window manner, such techniques are inefficient and costly. Recently, convolutional neural networks (CNNs) have successfully been used for object detection, and they have demonstrated considerably superior performance than that of traditional detection methods; however, this success has not been expanded to aerial images. To overcome such problems, we propose a detection model based on two CNNs. One of the CNNs is designed to propose many object-like regions that are generated from the feature maps of multi scales and hierarchies with the orientation information. Based on such a design, the positioning of small size objects becomes more accurate, and the generated regions with orientation information are more suitable for the objects arranged with arbitrary orientations. Furthermore, another CNN is designed for object recognition; it first extracts the features of each generated region and subsequently makes the final decisions. The results of the extensive experiments performed on the vehicle detection in aerial imagery (VEDAI) and overhead imagery research data set (OIRDS) datasets indicate that the proposed model performs well in terms of not only the detection accuracy but also the detection speed.

Arbitrary-angle bounding box based location for object detection in remote sensing image

“…In (Chen et al, 2019), expect for minimizing the classification error, Cheng et al impose a rotationinvariant regularizer and a Fisher discrimination regularizer on the FC7 layer of VGGNet-16 (Simonyan & Zisserman, 2014) to enforce the CNN features to be rotation-invariant and have powerful discriminative capability. In (Li et al, 2020), an unified object detection framework is proposed for combining the RPN and the contextual feature fusion network to extract the proposals and to simultaneously locate the geospatial objects.…”

“…We now compare the performance of our method with the four state-of-the-art approaches Faster R-CNN (Ren et al, 2017), RIFD-CNN (Chen et al, 2019), YOLOv4 (Bochkovskiy et al, 2020), and R2CNN (Jiang et al, 2017). We present the detection results of different object detection algorithms on the two datasets in Tables 7 and Tables 8. Generally, we can see from the table that compared to other comparison algorithms, our R-FRCNN algorithm has the lowest MAR and FAR on both data sets, and its F1 index is the highest.…”

Sparse Label Assignment for Oriented Object Detection in Aerial Images

“…In recent years, many rotation detectors have been proposed to introduce the additional orientation prediction to detect arbitrary-oriented objects in aerial images [8][9][10][11][12][13][14][15]. These detectors first densely preset a large number of prior boxes (also called anchors) to align with the ground-truth (GT) objects.…”

Small-Object Detection in UAV-Captured Images via Multi-Branch Parallel Feature Pyramid Networks

“…Recently, many methods [21][22][23][24][25][26] have been proposed and try to solve the issue of small objects. However, in the UAV-captured image, the object detection based on deep learning still faces severe challenges.…”