Search

Convolutional neural networks (CNNs) have been widely used for synthetic aperture radar (SAR) target detection. Typical methods based on CNN have obtained favorable detection accuracy at the cost of high model complexity, and thus are difficult to be directly applied to real-time satellites on board as well as maritime rescue. To deal with this problem, this article proposes an efficient and lightweight target detection network incorporating soft quantization. First, to compensate for the lack of accuracy caused by lightweight networks, a feature fusion module called split bidirectional feature pyramid network is proposed to alleviate the interference of complex background on SAR images. Meanwhile, to adapt the lightweight network and the feature fusion module, a linear transformation module is presented to enhance the linear representation of the model via learnable parameters. Eventually, to make the model size smaller, a soft quantization algorithm is proposed to reduce the accuracy degradation caused by quantization errors. We validate the robustness of the model in several publicly available datasets. Experimental results show that our model achieves 97.0% detection accuracy on SAR ship detection dataset, with a 0.9% accuracy improvement compared to mainstream methods using less than $15\times $ the number of parameters and less than $6\times $ the number of flops. [ABSTRACT FROM AUTHOR]

Highlights • We propose an automatic suspicious object detection and recognition method for THz security images. • We employed the advanced CNN model to achieve intelligent analysis. • The spatio-temporal context information is explored via SLD model for performance refinement. Abstract Owing to low radiation required for human body, complete detection of hazardous materials, noncontact privacy protection, and fast pass capability, terahertz (THz) security screening cameras (TSSCs) are widely deployed in public places. However, the existing TSSCs only report about the rough locations of suspicious objects, while directing the complicated detailed recognition work to security inspectors, thus leading to low recognition efficiency. In this paper, we aim to introduce the artificial intelligence technique, called convolutional neural network (CNN), with the spatio-temporal information of THz security image sequences, to achieve automatic object detection and recognition. Our method is composed of rough detection and detailed recognition. In rough detection, benefiting from the inherent alignment of human parts, the sparse and low-rank decomposition (SLD) is used to excavate the spatio-temporal context information. Specifically, the low-rank part representing the static background is regarded as the human body, while the sparse part representing the displaced target is regarded as the suspicious object. Then, by considering the shape knowledge and performing morphological processing, noise interference was reduced and the rough locations of suspicious objects were determined. In detailed recognition, supervised training was first conducted based on Faster R-CNN model with large-scale object labels. The trained Faster R-CNN could extract high-level semantic features of each anchor, and thus predict the class attribute of each object. Notably, with the results of rough detection, only anchors in the candidate domain are computed. Compared with the conventional full domain computation, this narrow-band approach not only reduces the computational complexity but also decreases the false positives caused by anchors in the background. Extensive experiments were conducted on THz security images, and the results prove that our method achieves high performance with respect to both accuracy and efficiency. [ABSTRACT FROM AUTHOR]