Novel Asymmetric Pyramid Aggregation Network for Infrared Dim and Small Target Detection

Abstract

Robust and efficient detection of small infrared target is a critical and challenging task in infrared search and tracking applications. The size of the small infrared targets is relatively tiny compared to the ordinary targets, and the sizes and appearances of the these targets in different scenarios are quite different. Besides, these targets are easily submerged in various background noise. To tackle the aforementioned challenges, a novel asymmetric pyramid aggregation network (APANet) is proposed. Specifically, a pyramid structure integrating dual attention and dense connection is firstly constructed, which can not only generate attention-refined multi-scale features in different layers, but also preserve the primitive features of infrared small targets among multi-scale features. Then, the adjacent cross-scale features in these multi-scale information are sequentially modulated through pair-wise asymmetric combination. This mutual dynamic modulation can continuously exchange heterogeneous cross-scale information along the layer-wise aggregation path until an inverted pyramid is generated. In this way, the semantic features of lower-level network are enriched by incorporating local focus from higher-level network while the detail features of high-level network are refined by embedding point-wise focus from lower-level network, which can highlight small target features and suppress background interference. Subsequently, recursive asymmetric fusion is designed to further dynamically modulate and aggregate high resolution features of different layers in the inverted pyramid, which can also enhance the local high response of small target. Finally, a series of comparative experiments are conducted on two public datasets, and the experimental results show that the APANet can more accurately detect small targets compared to some state-of-the-art methods.

An Infrared Maritime Small Target Detection Algorithm Based on Semantic, Detail, and Edge Multidimensional Information Fusion

“…Traditional algorithms such as Tophat [8], Maxmean [9], MPCM [11], IPI [12], SRWS [13], and PSTNN [14] are implemented on an Intel i7-12700 CPU with 32 GB of memory using Matlab R2021b. The specific experimental parameters for traditional algorithms are shown in Table 3 [47].…”

Moving Point Target Detection Based on Temporal Transient Disturbance Learning in Low SNR

“…In recent years, with the development of deep learning, point target detection algorithms based on convolutional neural networks have emerged endlessly, including ALCNet, GLFM, ISTDU, ISTNet, MLCL, and APANet [18][19][20][21][22][23]. The principles of these CNN-based methods are predominantly similar to those of traditional methods.…”