A Deep Convolutional Network for Multitype Signal Detection and Classification in Spectrogram

Li, Weihao; Wang, Keren; You, Liangzhi

doi:10.1155/2020/9797302

Cited by 20 publications

(19 citation statements)

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“…Moreover, their center point-based detection is not suitable for horizontally long signals, which leads to incomplete BBox proposals. To make up for the above shortcomings, in [10], we proposed a centerline-based neural network that models the signal based on its centerline and corresponding properties, other than the candidate anchors, and we achieved state-of-the-art performance for multi-type signal detection in spectrograms.…”

Section: A Morse Detectionmentioning

confidence: 99%

“…Shared convolution is used to extract shared features for subsequent detection and recognition branches. The backbone of the shared convolution is the same as in [10], which is a ResNet18 network [29] combined with three up-convolutions. Fig.…”

Section: A Overall Architecturementioning

confidence: 99%

“…In addition, it raises too many candidate anchors to regress, costing much time, and the anchor size is difficult to determine because of the dramatic change in the length of signals. Inheriting from the SSD, we targeted the characteristics of the signals and proposed an improved detector in our earlier work [10]. The detector first finds the centerline of the signal in a heat map, whose receptive field could cover the whole signal, and then, it predicts BBox size directly at the centerline points, thus abandoning the anchors, which proposes a more intact BBox and simplifies the model to greatly speed up.…”

Section: Introductionmentioning

confidence: 99%

“…It can be seen that DL-based methods have achieved stateof-the-art performance in both detection and recognition of Morse [10], [12]. However, the two neural networks are trained separately, which means that the character level information in the recognition that could help improve the detection effect is ignored by the detection model.…”

Section: Introductionmentioning

confidence: 99%

“…Inspired by the FOTS method [13], which is a typical text spotting network that combines two text detection and recognition networks to obtain better and faster performance, we propose to combine the Morse detection and recognition networks into a unified network named MorseNet and implement end-to-end training. Based on the detection model in [10], we add the CRNN model [12] after the feature extraction CNNs as a recognition branch. Thus, the feature extraction CNNs become a shared convolution, which supplies shared features to both the detection and recognition branches.…”

Section: Introductionmentioning

confidence: 99%

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MorseNet: A Unified Neural Network for Morse Detection and Recognition in Spectrogram

Wang²,

You

2020

IEEE Access

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Short-wave radio is an indispensable long-distance means of communication, among which Morse signals, which rely on simplicity and efficiency, plays an import role in military and civilian applications. Automatic Morse detection and recognition have been researched for several years, but some thorny problems in actual communication always restrict the performance of methods. In this paper, by introducing deep learning technology, we propose a network named MorseNet that can simultaneously locate and decode Morse signals in the spectrogram. MorseNet uses shared convolutions to extract shared features for both the detection and recognition branches. The detection branch regresses bounding boxes based on signal centerlines, and the recognition branch decodes Morse fragments cropped from feature maps by a convolutional recurrent neural network (CRNN). The losses of two branches are combined to implement the end-to-end training. Experimental results on four "simulated Morse + real background" datasets demonstrate that the proposed method achieves state-of-the-art performance in both detection and recognition, and it effectively improves four problems that have long been troublesome in accomplishing the tasks. Furthermore, the joint training strategy and architecture give MorseNet advantages over its twostage deployment in terms of accuracy, speed, and model size.

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Section: A Morse Detectionmentioning

confidence: 99%

Section: A Overall Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MorseNet: A Unified Neural Network for Morse Detection and Recognition in Spectrogram

Wang²,

You

2020

IEEE Access

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YFDM: YOLO for detecting Morse code

Wei,

Li,

Han

2023

Sci Rep

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With the increasing complexity of the shortwave communication environment, the efficiency and accuracy of the manual detection of Morse code no longer meet actual needs. Therefore, this paper proposes a Morse code detection algorithm called YFDM. For the time–frequency image of the received signal, a combination module of deformable convolution and C3 is used to enhance the backbone network’s attention to the abstract semantics and location information of Morse code. GSConv and VOV-GSCSP modules are used to build a lightweight neck network. Finally, the confidence propagation cluster (CP-Cluster) algorithm is used to filter the detection frame. In an ablation experiment, the parameters and giga floating-point operations per second (GFLOPs) of YFDM were 5.961 M and 9.74 G, respectively, 15.11% and 38.9% less than those of YOLOv5. Moreover, when WIoUv1 was used as the loss function of the bounding box, the AP0.5:0.95 and frames per second (FPS) values of the algorithm reached the highest values, 0.68 and 72.4. The experimental results indicate that the algorithm can effectively reduce the weight of the model while ensuring the detection accuracy and inference speed.

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Employing a multi-sensor fusion array to detect objects for an orbital transfer vehicle to remove space debris

Jani

2023

IJIUS

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PurposeThis article takes into account object identification, enhanced visual feature optimization, cost effectiveness and speed selection in response to terrain conditions. Neither supervised machine learning nor manual engineering are used in this work. Instead, the OTV educates itself without instruction from humans or labeling. Beyond its link to stopping distance and lateral mobility, choosing the right speed is crucial. One of the biggest problems with autonomous operations is accurate perception. Obstacle avoidance is typically the focus of perceptive technology. The vehicle's shock is nonetheless controlled by the terrain's roughness at high speeds. The precision needed to recognize difficult terrain is far higher than the accuracy needed to avoid obstacles.Design/methodology/approachRobots that can drive unattended in an unfamiliar environment should be used for the Orbital Transfer Vehicle (OTV) for the clearance of space debris. In recent years, OTV research has attracted more attention and revealed several insights for robot systems in various applications. Improvements to advanced assistance systems like lane departure warning and intelligent speed adaptation systems are eagerly sought after by the industry, particularly space enterprises. OTV serves as a research basis for advancements in machine learning, computer vision, sensor data fusion, path planning, decision making and intelligent autonomous behavior from a computer science perspective. In the framework of autonomous OTV, this study offers a few perceptual technologies for autonomous driving in this study.FindingsOne of the most important steps in the functioning of autonomous OTVs and aid systems is the recognition of barriers, such as other satellites. Using sensors to perceive its surroundings, an autonomous car decides how to operate on its own. Driver-assistance systems like adaptive cruise control and stop-and-go must be able to distinguish between stationary and moving objects surrounding the OTV.Originality/valueOne of the most important steps in the functioning of autonomous OTVs and aid systems is the recognition of barriers, such as other satellites. Using sensors to perceive its surroundings, an autonomous car decides how to operate on its own. Driver-assistance systems like adaptive cruise control and stop-and-go must be able to distinguish between stationary and moving objects surrounding the OTV.

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A Deep Convolutional Network for Multitype Signal Detection and Classification in Spectrogram

Cited by 20 publications

References 47 publications

MorseNet: A Unified Neural Network for Morse Detection and Recognition in Spectrogram

MorseNet: A Unified Neural Network for Morse Detection and Recognition in Spectrogram

YFDM: YOLO for detecting Morse code

Employing a multi-sensor fusion array to detect objects for an orbital transfer vehicle to remove space debris

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