Residual Depth Feature-Extraction Network for Infrared Small-Target Detection

Abstract

Deep-learning methods have exhibited exceptional performance in numerous target-detection domains, and their application is steadily expanding to include infrared small-target detection as well. However, the effect of existing deep-learning methods is weakened due to the lack of texture information and the low signal-to-noise ratio of infrared small-target images. To detect small targets in infrared images with limited information, a depth feature-extraction network based on a residual module is proposed in this paper. First, a global attention guidance enhancement module (GAGEM) is used to enhance the original infrared small target image in a single frame, which considers the global and local features. Second, this paper proposes a depth feature-extraction module (DFEM) for depth feature extraction. Our IRST-Involution adds the attention mechanism to the classic Involution module and combines it with the residual module for the feature extraction of the backbone network. Finally, the feature pyramid with self-learning weight parameters is used for feature fusion. The comparative experiments on three public datasets demonstrate that our proposed infrared small-target detection algorithm exhibits higher detection accuracy and better robustness.

Lightweight Tunnel Defect Detection Algorithm Based on Knowledge Distillation

“…To address this problem, model compression, and acceleration techniques have become popular areas of research. Among these, the knowledge distillation algorithm [16,[22][23][24][25] is a very effective model compression and acceleration technique that extracts knowledge from large, complex deep teacher models and passes it on to small, simple student models. This approach can significantly reduce the complexity and computational cost of student models, while still maintaining model performance.…”

Accurate Tracking Algorithm for Cluster Targets in Multispectral Infrared Images

“…(1) The full connection layer is replaced with a convolution layer to achieve end-to-end convolution network training. (2) To achieve pixel-level segmentation, all pixel features in the image are classified by prediction. However, for an image with a complicated visual environment, the FCN network still adopts the simplest deconvolution method, which results in blurred contours and serious adhesion of the segmented image.…”

“…Among the challenges faced in this field are the low signal-to-noise ratio imaging of cluster weak targets in complex environments, where they tend to be submerged in complex backgrounds. Moreover, the uneven intensity of IR images further diminishes the recognition rates and tracking accuracy of multiple small targets [2]. Consequently, achieving fine target tracking in infrared images has become a pressing technical challenge.…”