Implementation of Real-time Detection Algorithm for Space Debris Based on Multi-core DSP

Sun, Quan; Niu, Zhao; Yao, Chaoyun

doi:10.1088/1742-6596/1335/1/012003

Cited by 5 publications

(5 citation statements)

References 3 publications

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“…In the meantime, DSP and ARM processors with computing capability, flexibility, and large-scale integration have been adopted to implement the vision and image processing algorithms [37]. Sun et al [38] described an onboard space debris detection approach on a multi-core DSP platform that can process a 2048 × 2048 image in 600 ms. The parallelism of this system constrains the throughput of processing data streams, making it challenging to process intensive computing with large data volumes.…”

Section: Related Workmentioning

confidence: 99%

Implementation of Real-Time Space Target Detection and Tracking Algorithm for Space-Based Surveillance

Chen

Liu

et al. 2023

Remote Sensing

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Space-based target surveillance is important for aerospace safety. However, with the increasing complexity of the space environment, the stellar target and strong noise interference pose difficulties for space target detection. Simultaneously, it is hard to balance real-time processing with computational performance for the onboard processing platform owing to resource limitations. The heterogeneous multi-core architecture has corresponding processing capabilities, providing a hardware implementation platform with real-time and computational performance for space-based applications. This paper first developed a multi-stage joint detection and tracking model (MJDTM) for space targets in optical image sequences. This model combined an improved local contrast method and the Kalman filter to detect and track the potential targets and use differences in movement status to suppress the stellar targets. Then, a heterogeneous multi-core processing system based on a field-programmable gate array (FPGA) and digital signal processor (DSP) was established as the space-based image processing system. Finally, MJDTM was optimized and implemented on the above image processing system. The experiments conducted with simulated and actual image sequences examine the accuracy and efficiency of the MJDTM, which has a 95% detection probability while the false alarm rate is 10−4. According to the experimental results, the algorithm hardware implementation can detect targets in an image with 1024 × 1024 pixels in just 22.064 ms, which satisfies the real-time requirements of space-based surveillance.

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Section: Related Workmentioning

confidence: 99%

Implementation of Real-Time Space Target Detection and Tracking Algorithm for Space-Based Surveillance

Chen

Liu

et al. 2023

Remote Sensing

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“…It consists of three steps: the first step is to remove the background by using The top hat method, the second step is to remove stars by using a masking method, and the final step is to detect the SD by using a weighted model. Niu et al [11] proposed a real time streak detection technique using the multicore DSP. It can detect weak and faint celestial objects and SD and it includes two processes the first one is the SD detection technique and the second one is the DSPs' scheme.…”

Section: Traditional Streak Detectionmentioning

confidence: 99%

Streak detection in astronomical images based on convolutional neural network

Elhakiem,

Ghoniemy,

Salama

2023

J. Phys.: Conf. Ser.

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Streak detection is important in space situational awareness and space asset protection. It is desirable to detect moving targets (e.g., satellites, space debris, or meteorites) in images of the sky. This paper presents a comparison between two astronomical frameworks for streak detection based on deep CNNs. The first framework uses the extended feature pyramid network (EFPN) with faster region-based CNNs (Faster R-CNN) and compares it with the second framework that uses the feature pyramid network (FPN) with Faster R-CNN. Because there aren’t enough publicly available astronomical data sets, we use the simulated data set to train the neural network. The metrics of mean average precision (mAP), recall, precision, and F1 score were used to measure the performance of the two frameworks. The experimental results confirmed that the EFPN-based framework achieves a significant improvement in streak detection than the Faster R-CNN framework based on the FPN model.

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“…The erosion and dilation operations of flat structural elements involved in the open operation are simplified to the minimum and maximum filters with the support domain of structural elements as the window respectively, which greatly reduces the algebraic computation. In order to further improve the processing speed and take into account the portability of the algorithm, flat structure elements of 1×25 and 25×1 are decomposed into small size structure elements of 1×3 and 3×1 [36], as shown Figure 2. If the image size is m×n, the computational complexity of the background suppression algorithm is O(mn).…”

Section: Gso Debris Extractormentioning

confidence: 99%

An Adaptive Real-Time Detection Algorithm for Dim and Small Photoelectric GSO Debris

Sun

Niu

Li³

et al. 2019

Sensors

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Geosynchronous orbit (GSO) is the ideal orbit for communication, navigation, meteorology and other satellites, but the space of GSO is limited, and there are still a large number of space debris threatening the safety of spacecraft. Therefore, real-time detection of GSO debris is necessary to avoid collision accidents. Because radar is limited by transmitting power and operating distance, it is difficult to detect GSO debris, so photoelectric detection becomes the mainstream way to detect GSO debris. This paper presents an adaptive real-time detection algorithm for GSO debris in the charge coupled device (CCD) images. The main work is as follows: An image adaptive fast registration algorithm and an enhanced dilation difference algorithm are proposed. Combining with mathematical morphology, threshold segmentation and global nearest neighbor (GNN) multi-target tracking algorithm, the functions of image background suppression, registration, suspected target extraction and multi-target tracking are realized. The processing results of a large number of measured data show that the algorithm can detect dim geostationary earth orbit (GEO) and non-GEO debris in GSO belt stably and efficiently, and the processing speed meets the real-time requirements, with strong adaptive ability, and has high practical application value.

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Implementation of Real-time Detection Algorithm for Space Debris Based on Multi-core DSP

Cited by 5 publications

References 3 publications

Implementation of Real-Time Space Target Detection and Tracking Algorithm for Space-Based Surveillance

Implementation of Real-Time Space Target Detection and Tracking Algorithm for Space-Based Surveillance

Streak detection in astronomical images based on convolutional neural network

An Adaptive Real-Time Detection Algorithm for Dim and Small Photoelectric GSO Debris

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