RESUMO
In recent years, object detection became more and more important following the successful results from studies in deep learning. Two types of neural network architectures are used for object detection: one-stage and two-stage. In this paper, we analyze a widely used two-stage architecture called Faster R-CNN to improve the inference time and achieve real-time object detection without compromising on accuracy. To increase the computation efficiency, pruning is first adopted to reduce the weights in convolutional and fully connected (FC) layers. However, this reduces the accuracy of detection. To address this loss in accuracy, we propose a reduced region proposal network (RRPN) with dilated convolution and concatenation of multi-scale features. In the assisted multi-feature concatenation, we propose the intra-layer concatenation and proposal refinement to efficiently integrate the feature maps from different convolutional layers; this is then provided as an input to the RRPN. Using the proposed method, the network can find object bounding boxes more accurately, thus compensating for the loss arising from compression. Finally, we test the proposed architecture using ZF-Net and VGG16 as a backbone network on the image sets in PASCAL VOC 2007 or VOC 2012. The results show that we can compress the parameters of the ZF-Net-based network by 81.2% and save 66% of computation. The parameters of VGG16-based network are compressed by 73% and save 77% of computation. Consequently, the inference speed is improved from 27 to 40 frames/s for ZF-Net and 9 to 27 frames/s for VGG16. Despite significant compression rates, the accuracy of ZF-Net is increased from 2.2% to 60.2% mean average precision (mAP) and that of VGG16 is increased from 2.6% to 69.1% mAP.
RESUMO
In this paper, we propose a switching bilateral filter (SBF) with a texture and noise detector for universal noise removal. Operation was carried out in two stages: detection followed by filtering. For detection, we propose the sorted quadrant median vector (SQMV) scheme, which includes important features such as edge or texture information. This information is utilized to allocate a reference median from SQMV, which is in turn compared with a current pixel to classify it as impulse noise, Gaussian noise, or noise-free. The SBF removes both Gaussian and impulse noise without adding another weighting function. The range filter inside the bilateral filter switches between the Gaussian and impulse modes depending upon the noise classification result. Simulation results show that our noise detector has a high noise detection rate as well as a high classification rate for salt-and-pepper, uniform impulse noise and mixed impulse noise. Unlike most other impulse noise filters, the proposed SBF achieves high peak signal-to-noise ratio and great image quality by efficiently removing both types of mixed noise, salt-and-pepper with uniform noise and salt-and-pepper with Gaussian noise. In addition, the computational complexity of SBF is significantly less than that of other mixed noise filters.
