Abstract:

In view of the problems of less texture details and low detection accuracy of infrared targets,we propose a Cascade-RCNN algorithm introducing an attention mechanism in thermal detection scenes,and design an attention mechanism suitable for infrared scenes.Because the attention mechanism is commonly used for performance verification on visible-light datasets,we first experiment the detection accuracy of other attention mechanisms on the thermal detection dataset,and meanwhile,propose an attention mechanism that interacts with explicit and implicit channels.In this method,the factorization machine method and the fully connected layer method are adopted,using this method to make all features go into the same Hilbert space.We propose a local pooling method to replace the global pooling method to obtain more image spatial information,using multi-scale convolution in the spatial dimension to extract target information in different receptive fields.An experiment is conducted on the FLIR thermal dataset.Without many parameters,the detection performance is improved by about 2% on different backbone networks compared to the Cascade R-CNN.

Key words: factorization machine, local pooling, decoupling structure, multi-scale convolution, thermal detection