Object detection for rotated and densely arranged objects in aerial images using path aggregated feature pyramid networks

Xiangyu Liu; Hong Pan; Xinde Li

doi:10.1117/12.2538090

14 February 2020 Object detection for rotated and densely arranged objects in aerial images using path aggregated feature pyramid networks

Xiangyu Liu, Hong Pan, Xinde Li

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Proceedings Volume 11430, MIPPR 2019: Pattern Recognition and Computer Vision; 114300J (2020) https://doi.org/10.1117/12.2538090
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

Object detection based on deep learning algorithms has been an important yet challenging research field in computer vision. The feature pyramid network has become a dominant network architecture in many detection applications because of its powerful feature learning ability for objects with varying scales. To address the challenges in detecting small and densely packed objects, this paper proposes an innovative object detection approach by combining the path aggregation scheme and the feature pyramid network into a unified framework. Specifically, we add a bottom-up branch with lateral connection onto the existing feature pyramid network and apply adaptive feature fusion strategy, which improves the detection performance for small and densely arranged objects in remote sensing images. Experiment results show that our proposed path aggregated feature pyramid network can improve the detection performance for diverse objects in aerial images.

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Xiangyu Liu, Hong Pan, and Xinde Li "Object detection for rotated and densely arranged objects in aerial images using path aggregated feature pyramid networks", Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114300J (14 February 2020); https://doi.org/10.1117/12.2538090

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KEYWORDS

Detection and tracking algorithms

Sensors

Feature extraction

Image fusion

Target detection

Network architectures

Data modeling

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