Abstract
As a precursor step for computer vision algorithms, object detection plays an important role in various practical application scenarios. With the objects to be detected becoming more complex, the problem of multi-scale object detection has attracted more and more attention, especially in the field of remote sensing detection. Early convolutional neural network detection algorithms are mostly based on artificially preset anchor-boxes to divide different regions in the image, and then obtain the prior position of the target. However, the anchor box is difficult to set reasonably and will cause a large amount of computational redundancy, which affects the generality of the detection model obtained under fixed parameters. In the past two years, anchor-free detection algorithm has achieved remarkable development in the field of detection on natural image. However, there is no sufficient research on how to deal with multi-scale detection more effectively in anchor-free framework and use these detectors on remote sensing images. In this paper, we propose a specific-attention Feature Pyramid Network (FPN) module, which is able to generate a feature pyramid, basing on the characteristics of objects with various sizes. In addition, this pyramid suits multi-scale object detection better. Besides, a scale-aware detection head is proposed which contains a multi-receptive feature fusion module and a size-based feature compensation module. The new anchor-free detector can obtain a more effective multi-scale feature expression. Experiments on challenging datasets show that our approach performs favorably against other methods in terms of the multi-scale object detection performance.
Highlights
In recent years, with the development of deep learning research, many convolutional neural networks (CNN)-based detection frameworks were proposed and they improve the detection performance in different aspects [1,2,3,4,5,6,7,8]
We propose a feature compensation module Size-based Feature Compensation module (SBFC), which can be weighted based on object size, to solve the problem that the anchor-free algorithm is insufficient to express the objects in the corresponding area based on the features on their key points
We find that using minmax instead of moment function to obtain pseudo-box from reppoints can obtain more reasonable key point positions and feature expressions of candidate regions in remote sensing image, which is inconsistent with conclusions in natural image detection tasks
Summary
With the development of deep learning research, many convolutional neural networks (CNN)-based detection frameworks were proposed and they improve the detection performance in different aspects [1,2,3,4,5,6,7,8] These methods have obtained impressive achievements in various application scenarios, complex scale variations in many real-world scenarios are still a fundamental challenge to achieve satisfied performance, such as ship or plane detection in remote sensing images [9,10], diseased organ detection in medical images, and traffic sign detection [11] of autonomous driving. When the network solves multi-scale object detection problems, these methods extract better multi-scale features through well-designed network structures, or pre-process input data to simplify the learning of parameters
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