A feature‐optimized Faster regional convolutional neural network for complex background objects detection

Abstract

Abstract In recent years, convolutional neural networks are playing an increasingly important role in the field of object detection. However, the complex background of the detected image, the limited receptive field by the fixed geometry of the convolution kernel when building the model, and the positioning and pooling deviation from the region of interest are still important factors that affect the detection accuracy. In this paper, an improved algorithm is proposed for target detection based on Faster regional convolutional neural network. In the bounding box positioning phase, an improved interpolation algorithm‐Newton's parabolic interpolation is proposed instead of bilinear interpolation, after ROI size normalized by extending a parallel branch of tensor to weaken the negative impact of complex background on prospects in the phase of feature extraction using neural network recently popular attention CBAM mechanism model and the deformable convolution. Without bells and whistles, a series of experiments show that our method has higher target detection accuracy on the datasets PASCAL VOC2007, VOC2012, COCO 2014 and DIOR. Hence, the method is effective for actual target recognition tasks in complex background environments. The authors hope that the method will contribute to future research. Code has been made available at: https://github.com/liumaozhen‐lmz/Faster_R‐CNN_Attention.git.

Keywords

• Optical, image and video signal processing
• Image recognition
• Interpolation and function approximation (numerical analysis)
• Computer vision and image processing techniques
• Interpolation and function approximation (numerical analysis)