Wavelet RCNN: Enhancing Object detection accuracy through Spectral Information

We propose an object detection model which uses Wavelet transforms to address the trade-off between its speed and accuracy. The feature extraction network uses wavelet transforms to aid the object detector with spectral information and further reduce the parameters of the detection pipeline. The multi-resolution analysis performed by wavelet transforms decomposes image into high frequency and low frequency feature maps. The statistics of the frequency/spectral information at different scales and orientations define image features which improve accuracy of object localisation. The idea is to reduce the resolution of feature maps and increase receptive field size. The sparsity induced in feature maps by wavelet transforms is an efficient way to look for relevant features, thus significantly reducing pipeline parameters. This helps to improve the speed of the Wavelet RCNN model. To improve the classification of the model, traditional ROI pooling is replaced by wavelet pooling. This has been evaluated on the PASCAL VOC dataset on which it achieves the mAP of 75.7%. Model performance is evaluated using orthogonal wavelet (Haar) and biorthogonal wavelet (Bior3.5). A significant increase in speed with a slight increase in accuracy was achieved using orthogonal wavelets. While a significant increase in accuracy with slight increase in speed was achieved using Biorthogonal wavelets.