## 2 Applied Sensor Engineering in Logistic and Shipping Activities

Table 2: Landmark research on sensor engineering applied in logistic and shipping activities. Figure 6: Comparison of the evaluated detection performance among (**a**) CFAR, F-RCNN, and Coupled-CNN E A; (**b**) SSD, MFLHD, and the method proposed by [PERSON] et al. (redrawn based on data in [PERSON] et al. [64] and [PERSON] et al. [65]). Recently, research on performance evaluations in marine navigation systems was intensely reviewed by [PERSON] et al. [67]. The global navigation satellite system (GNSS) has been widely used for ship navigation, and spoofing poses an important threat to maritime logistics. [PERSON] et al. [67] conducted research on the global navigation satellite system (GNSS) by combining it with an inertial measurement unit (IMU) and a Doppler velocity log (DVL). An inertial measurement unit (IMU) can identify three-axis acceleration and three-axis angular velocity. On the other hand, a Doppler velocity log (DVL) can measure the speed of a ship. Based on this research, the method can achieve less than 1 km horizontal error in an hour. A critical research issue for ship traffic safety in narrow waters is the ship speed limit, which is one of the most common causes of maritime accidents. [PERSON] et al. [68] presented a new methodology to quantify the shipping traffic volume in the Yangtze River under current speed limits by using the automatic identification system (AIS) big data. The steps of the proposed method are as follows: (1) cleaning the raw AIS data to filter out unavoidable errors to improve the quality of the data, (2) geocoding the AIS data, (3) calculating the ship traffic aspect, and (4) estimating the shipping speed and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Milestone** & **Author(s)** & **Observational Subject** & **Tools/Instrument** & **Important Remarks** \\ \hline
2017 & [PERSON] et al. [57] & Oil spill detection in the sea territory & Multi-polarization synthetic aperture radar (SAR) images & The presence of both simulated and real data in the one-sided generalized likelihood ratio test (GLRT) confirms the practical effectiveness for oil detection. \\ \hline
2018 & [PERSON] et al. [56] & Vessel detection in the sea territory & Optical satellite imagery & The result shows that vessel monitoring from spaceborne optical images is a thriving research topic and will have excellent operational potential in the future due to a massive number of satellite data, much of it being free and open. \\ \hline
2018 & [PERSON] et al. [66] & Inshore ship detection & Synthetic aperture radar (SAR) & The result shows that inshorse ships can be detected using SAR images with reasonable accuracy and integrity. \\ \hline
2019 & [PERSON] et al. [65] & Ship detection on the surface of the sea & Multilayer fusion light-head detector (MFLHD) & The extensive experiments on the multilayer fusion light-head detector (MFLHD) achieved superior performance in SAR ship detection, in terms of both accuracy and speed. \\ \hline
2019 & [PERSON] et al. & Ship detection in the inland river & High-resolution optical satellite imagery & The result shows that using a mixture of multi-scale Deformable Part Models (DPMs) and a Histogram of Oriented Gradient (HOG) is effective in ship detection and performs properly in separating ships clustered together and those alongside the pier. \\ \hline
2020 & [PERSON] et al. [61] & Ship detection on the surface of the sea & Multiscale Wasserstein auxiliary classifier generative adversarial (MW-ACGAN) and the Yolo v3 network & The Multiscale Wasserstein Auxiliary Classifier Generative Adversarial Network (MW-ACGAN) can effectively generate realistic multiclass ship images. \\ \hline
2020 & [PERSON] et al. [64] & Ship detection on the surface of the sea & Synthetic aperture radar (SAR) & The novel detector based on convolution neural networks for multiscale SAR ship detection showed better performance compared to the method from the SAR ship detection dataset (SSDD). \\ \hline \hline \end{tabular}
\end{table} 
Table 2: \(Cont.\)traffic volume to analyze the speed limit.