Abstract
One-stage or two-stage deep learning based methods have problems when performing infrared multi-target detection, namely low accuracy and low running speed. Inspired by regression ideas of two-stage anchor-based model from coarse to fine, we propose a high performance pseudo-two-stage model that is specific to infrared images, in order to make a trade-off. The model retains the fast speed of the one-stage detection model through the introduction of cascade regression. We designed a dual-pass fusion module (DFM) and adaptive channel enhancement module (ACEM) to implement infrared image key feature fusion and calibration. To further optimize the model, we exploited the cascade regression and hard example mining by analyzing the shortcomings of the current one-stage detection approach. We conducted comparative experiments on the FLIR ADAS dataset and our method obtained 75.57% mAP, which is about 5% and 13% higher than the Faster R-CNN two-stage model and the SSD one-stage model, respectively. We ran the model at 21.4 FPS on Geforce RTX 2080 Ti, which made it 3FPS slower than SSD. The promising results show that the proposed approach is effective and practical.
Published Version
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