Object Detection based on OcSaFPN in Aerial Images with Noise
Object Detection based on OcSaFPN in Aerial Images with Noise
Taking the deep learning-based algorithms into account has become a crucial way to boost object detection performance in aerial images. While various neural network representations have been developed, previous works are still inefficient to investigate the noise-resilient performance, especially on aerial images with noise taken by the cameras with telephoto lenses, and most of the research is concentrated in the field of denoising.Of course, denoising usually requires an additional computational burden to obtain higher quality images, while noise-resilient is more of a description of the robustness of the network itself to different noises, which is an attribute of the algorithm itself. For this reason, the work will be started by analyzing the noise-resilient performance of the neural network, and then propose two hypotheses to build a noise-resilient structure. Based on these hypotheses, we compare the noise-resilient ability of the Oct-ResNet with frequency division processing and the commonly used ResNet. In addition, previous feature pyramid networks used for aerial object detection tasks are not specifically designed for the frequency division feature maps of the Oct-ResNet, and they usually lack attention to bridging the semantic gap between diverse feature maps from different depths. On the basis of this, a novel octave convolution-based semantic attention feature pyramid network (OcSaFPN) is proposed to get higher accuracy in object detection with noise. The proposed algorithm tested on three datasets demonstrates that the proposed OcSaFPN achieves a state-of-the-art detection performance with Gaussian noise or multiplicative noise. In addition, more experiments have proved that the OcSaFPN structure can be easily added to existing algorithms, and the noise-resilient ability can be effectively improved.
基于OcSaFPN的航空噪声图像目标检测。
考虑到基于深度学习的算法已成为提高航空图像中目标检测性能的关键方法。尽管已经开发了各种神经网络表示形式，但以前的工作仍然无法有效地研究抗噪性能，尤其是在带有远摄镜头的相机拍摄的带有噪声的航空图像上，并且大部分研究都集中在降噪领域。.. 当然，去噪通常需要额外的计算负担才能获得更高质量的图像，而抗噪声性更多地描述了网络本身对不同噪声的鲁棒性，这是算法本身的一个属性。因此，将通过分析神经网络的抗噪声性能开始这项工作，然后提出两个假设来构建抗噪声结构。基于这些假设，我们将Oct-ResNet的抗噪声能力与频分处理和常用的ResNet进行了比较。此外，以前用于空中物体检测任务的特征金字塔网络不是专门为Oct-ResNet的频分特征图设计的，而且他们通常不重视弥合来自不同深度的不同特征图之间的语义鸿沟。在此基础上，提出了一种基于八度卷积的新型语义注意特征金字塔网络（OcSaFPN），以提高噪声检测对象的准确性。在三个数据集上测试的拟议算法证明，拟议的OcSaFPN具有高斯噪声或乘性噪声，可实现最新的检测性能。另外，更多的实验证明，OcSaFPN结构可以很容易地添加到现有算法中，并且可以有效地提高抗噪能力。在三个数据集上测试的拟议算法证明，拟议的OcSaFPN具有高斯噪声或乘性噪声，可实现最新的检测性能。另外，更多的实验证明，OcSaFPN结构可以很容易地添加到现有算法中，并且可以有效地提高抗噪能力。在三个数据集上测试的拟议算法证明，拟议的OcSaFPN具有高斯噪声或乘性噪声，可实现最新的检测性能。另外，更多的实验证明，OcSaFPN结构可以很容易地添加到现有算法中，并且可以有效地提高抗噪能力。 （阅读更多）
