Infrared Target Detection Algorithm Based on Strip Pooling and Attention Mechanism in Street Scene

Abstract

Infrared image in street scene contains less detail information and complex background, the existing target detection model exhibits low accuracy and sluggish processing speed. To address these issues, a new infrared target detection algorithm based on strip pooling and attention mechanism is proposed.The Mixed Pooling Module(MPM) includes strip pooling and the Pyramid Pooling Module(PPM) is used to improve the Spatial Pyramid Pooling Fast (SPPF) module. Strip pooling is applied to solve the feature loss and pollution issues existing in the traditional pooling operation during target detection, so as to improve the feature extraction ability for long and narrow targets, and the global dependency relationship is established between isolated targets, whereby this new method helps the model capture more enriched feature information. The global pooling operates in the horizontal direction, and vertical directions are handled by the attention module to obtain the position information of the target in the global range of the feature map, whereby the position information is embedded into the feature channel so that the algorithm can locate the target more accurately and reduce the impact of complex backgrounds on detection performance.Batch-Free Normalization(BFN) is used to address the performance degradation caused by the accumulation of the estimated offset in Batch Normalization(BN), which further improves the detection performance of the algorithm.The experimental results on FLIR dataset show that the improved algorithm has an mAP(IoU value is 0.5) of 80.7% and an F1 value of 78.0%, which are 1.9 and 2.4 percentage points higher than those of YOLOv5, respectively.