In order to detect space weak targets in low signal-to-clutter ratio (SCR) environment, this letter presents an effective detection model. At the first stage, two-dimensional least-mean-square preprocessing part is applied to the original image, after which, the suspicious area can be obtained. At the second stage, a novel method based on the contrast mechanism of human visual system called neighborhood saliency map is applied, which improves the local contrast map algorithm and greatly increases the accuracy of neighborhood saliency estimation, meanwhile, effectively enhances targets and suppresses background. Then, a simple threshold segmentation is used to get real targets. Compared with other state-of-the-art algorithms, the proposed algorithm obtains the superior performance in terms of SCR gain, background suppression factor, and detection results (detection rate and false alarm rate). The model proposed in this letter can effectively detect spatial weak point targets in the case of average SCR ≈ 1 or even SCR < 1 (with minimum SCR is about 0.55).

To address the problem of detecting dim small targets against complex moving backgrounds, based on spatiotemporal saliency features, this paper proposes a novel extraction model: the temporal and spatial saliency feature (TSSF) model. In the temporal domain processing stage, a temporal motion saliency operator is constructed by leveraging the differences in temporal motion responses between the target and the background; this operator simultaneously filters for candidate target points while suppressing large-area background clutter. In the spatial domain processing stage, a max-mean-based grayscale saliency operator is constructed to further validate the candidate target points. Building upon target enhancement processing, target trajectories can be effectively extracted through multi-frame association. Quantitative experiments on ten test sequences with diverse background complexities and signal-to-clutter ratios (SCRs) of around 1 or lower show that the TSSF model achieves optimal background suppression factors (BSFs) on seven sequences and the maximum SCR gain (SCRG) across eight sequences, with an average SCRG of roughly 330. For detection performance assessed via ROC curves, the proposed method obtains the highest AUC values on nine test sequences. The consistently superior performance validates the effectiveness and generalizability of the proposed TSSF model for dim small target enhancement, background suppression, and robust target detection.

Through investigating the status of information management of laboratory equipment in a certain aerospace scientific research unit, analyzing the backwardness of its equipment information management mode, low efficiency and high error rate, this paper puts forward the application of RFID(Radio Frequency Identification) technology in the laboratory, combining visualization, data mining technology and building intelligent equipment information management system. The paper focuses on five functional modules of the system: equipment in-position management, equipment flow information management, equipment resource intelligent allocation management, equipment rights management and equipment life cycle intelligent management. Meanwhile, key technologies involved such as: middleware, mathematical modeling, frequent pattern mining are highlighted. The system has been successfully implemented in an aerospace laboratory and intelligent equipment information management has been realized.