At present, the polar region is used to monitor and investigate fish by combining sonar detection with artificial fishing statistics, which is limited by economic cost, operation area and time. Object detection algorithms based on deep learning can identify and detect fish while meeting economic requirements, but traditional object detection algorithms often have many parameters and calculations and cannot adapt to the harsh conditions of energy consumption and storage limitations in the polar region. To solve this problem, an improved lightweight fish detection algorithm for YOLOv8n was proposed, in which the GhostC2f module was used to replace C2f in the backbone and neck networks, and GhostConv was used to replace part of the Conv in the network, and the EMA attention mechanism was introduced in the backbone network to improve the feature extraction ability. Finally, the MPDIOU loss function, which is simpler in the calculation process, was used to replace the CIOU to improve the detection speed. Experiments on the self-made fish dataset show that the number of parameters and computation of the improved algorithm become 1.49M and 4.7GFLOPs, respectively, and only 49.67% of the parameters of the original YOLOv8n are used to achieve the same detection accuracy, which meets the requirements of model deployment under limited hardware conditions.
KEYWORDS: Ice, Detection and tracking algorithms, Radar signal processing, Signal detection, Radar sensor technology, Radar, Computer simulations, Spectrum analysis, Signal processing, Signal analyzers
The Antarctic ice sheet is an important part of the Earth system and plays a key role in the global climate system. The frequency modulated continuous wave (FMCW) radar can be used to obtain the changes of the transmitted signals at different layers inside the ice sheet, and then the information of the isochronous layers of the ice sheet can be extracted and the shallow accumulation rate can be deduced. In this paper, an improved spectrum estimation algorithm is proposed, which combines FFT (Fast Fourier Transform) and Chirp-Z (CZT transform, linear frequency modulation transform) to estimate the spectrum of the simulated radar system's difference frequency signals, effectively improving the radar detection accuracy, and verifying the effectiveness and rationality of the algorithm, so that the isochronous layers can be extracted on a larger spatial scale. In China's 36th Antarctic scientific expedition, FMCW radar was used to obtain the distribution of shallow isochronous layers in East Antarctica from Zhongshan Station to Taishan Station. In this paper, taking a typical area as an example, the algorithm was used to obtain the stratification and structure information of the ice sheet on a large-scale space, which provides important data support for the calculation of large-scale accumulation rate and flow history in the Antarctic region.
In complex outdoor environments, mobile robots may experience problems such as signal loss, low accuracy, and difficulty in localization and navigation. To address the limitations of using a single sensor for target detection and map building, as well as the lack of research on robot localization and navigation in complex outdoor environments, this paper proposes a method for integrating information from multiple sensors for outdoor robot localization and navigation. Based on multiple sensors such as lidar, IMU, GPS, and camera, data fusion is performed using Kalman filtering to effectively address the shortcomings of traditional single-sensor methods, including the cumbersome preliminary work and low localization and navigation accuracy. Simulation results demonstrate that this fusion localization and navigation algorithm can achieve complementary advantages between different sensors, with strong anti-interference capabilities and high localization accuracy. By using multiple sensors to provide correction information and reduce the possibility of measurement errors, this method provides theoretical support for robot movement and localization in complex environmental scenarios.
Three novel compounds called 3’,3”-dodecyl-2-sulfonyl phthalein (DSP), 3’, 3”-myristyl-2-sulfonyl phthalein (MSP) and 3’,3”-dodecyl-5’,5’’-nitro-2-sulfonyl phthalein (DNSP) were synthesized to play as functional dyes in both chemical and biological areas. The as-synthesized chromotropic dyes were characterized by NMR, elemental analysis and pH value tester also the allochroic behaviors and surface activity of the new compounds were tested and performed in details mainly with the optical contact angle measurement and UV-Vis spectrophotometer. The introduction of the long chain alkyl groups into the skeleton is aiming to increase the surface activity of those dyes while that of the nitro group was focused on the regulation of the color transition point (CTP). The results indicated that the target dyes showed satisfying properties in not only the chromogenic performance but also the surface activity. Therefore, it may be an effective and durable functional dye with further potential applications in the field of pH sensors, probe and coating materials.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.