Heterogeneous Gray-Temperature Fusion-Based Deep Learning Architecture for Far Infrared Small Target Detection

Abstract

This paper proposes the end-to-end detection of a deep network for far infrared small target detection. The problem of detecting small targets has been a subject of research for decades and has been applied mainly in the field of surveillance. Traditional methods focus on filter design for each environment, and several steps are needed to obtain the final detection result. Most of them work well in a given environment but are vulnerable to severe clutter or environmental changes. This paper proposes a novel deep learning-based far infrared small target detection method and a heterogeneous data fusion method to solve the lack of semantic information due to the small target size. Heterogeneous data consists of radiometric temperature data (14-bit) and gray scale data (8-bit), which includes the physical meaning of the target, and compares the effects of the normalization method to fuse heterogeneous data. Experiments were conducted using an infrared small target dataset built directly on the cloud backgrounds. The experimental results showed that there is a significant difference in performance according to the various fusion methods and normalization methods, and the proposed detector showed approximately 20% improvement in average precision (AP) compared to the baseline constant false alarm rate (CFAR) detector.

[Retracted] Target Adaptive Tracking Based on GOTURN Algorithm with Convolutional Neural Network and Data Fusion

“…Literature [11] directly inputs the image into the convolutional neural network to extract the target feature and then uses the feature to train the classifier to distinguish whether the feature is a positive sample or a negative sample. Literature [12] extracts the first-layer features of the VGG network as the target feature and integrates it into the framework of SRDCF to improve the performance of SRDCF. Because the deep network focuses on different points, low-level features pay more attention to detailed information, and high-level features pay more attention to semantic information.…”

A Method for Infrared Sea-Sky Condition Judgment and Search System: Robust Target Detection via PLS and CEDoG

“…Local Contrast Method [17]- [20] Spatio-temporal Saliency Approach [21]- [22] Low-rank Tensor Completion [23]- [24] Based on Deep Learning [25]- [29] It can be known from above scientific research situation that these current methods are more suitable for targets with a single feature in the entire image [30][31][32][33][34]. On the other word, sea-sky background clutter and wave noise in the space or transform domain are irrelevant to the target [35][36][37][38][39][40][41].…”

Application of Deep Convolutional Neural Network Under Region Proposal Network in Patent Graphic Recognition and Retrieval

“…To verify that the VGG-16 network model [6] is better than Convolution Sparse Filter Learning (CSFL), the three types of images are used to test the VGG-16 network model and the CSFL network model. This scheme is used to reduce the candidate area during the test, and the result is shown in Figure 13.…”

“…The directional gradient histogram detection method can effectively express the target's outline structure features and shape features. The commonly used method combines the directional gradient histogram feature method and the support vector machine to detect the target [6]. Combining candidate borders with convolutional neural network (CNN) makes target detection a real end-to-end model structure.…”