I‐CenterNet: Road infrared target detection based on improved CenterNet

Abstract

Abstract Infrared target detection has strong anti‐interference ability, long working distance and can work day and night. So it is widely used in military security and transportation fields, and infrared road object detection is critical in traffic checkpoints and autonomous driving. However, the target scale in infrared images changes greatly, small targets are difficult to detect, the poor image quality and low signal‐to‐noise ratio are still huge challenges in infrared target detection. This paper proposes an improved infrared target detection model I‐CenterNet based on the anchor‐free model CenterNet. The EfficientNetV2 with the channel attention mechanism is used instead of the traditional structure as the backbone network to enhance feature extraction. In order to reduce the noise of the input infrared image, Dilated‐Residual U‐net (DRUNet) is used. Meanwhile, feature pyramid and Sub‐Pixel are combined for multi‐scale feature fusion. Data enhancement is implemented to improve model performance. The experimental results show that the average detection accuracy of this model on the Flir infrared data set is 87.9%, and the average detection speed reaches 14.2 frames/s.

Keywords

• Image recognition
• Computer vision and image processing techniques
• Military administration
• Traffic engineering computing