Video-Based Detection Infrastructure Enhancement for Automated Ship Recognition and Behavior Analysis

Abstract

Video-based detection infrastructure is crucial for promoting connected and autonomous shipping (CAS) development, which provides critical on-site traffic data for maritime participants. Ship behavior analysis, one of the fundamental tasks for fulfilling smart video-based detection infrastructure, has become an active topic in the CAS community. Previous studies focused on ship behavior analysis by exploring spatial-temporal information from automatic identification system (AIS) data, and less attention was paid to maritime surveillance videos. To bridge the gap, we proposed an ensemble you only look once (YOLO) framework for ship behavior analysis. First, we employed the convolutional neural network in the YOLO model to extract multi-scaled ship features from the input ship images. Second, the proposed framework generated many bounding boxes (i.e., potential ship positions) based on the object confidence level. Third, we suppressed the background bounding box interferences, and determined ship detection results with intersection over union (IOU) criterion, and thus obtained ship positions in each ship image. Fourth, we analyzed spatial-temporal ship behavior in consecutive maritime images based on kinematic ship information. The experimental results have shown that ships are accurately detected (i.e., both of the average recall and precision rate were higher than 90%) and the historical ship behaviors are successfully recognized. The proposed framework can be adaptively deployed in the connected and autonomous vehicle detection system in the automated terminal for the purpose of exploring the coupled interactions between traffic flow variation and heterogeneous detection infrastructures, and thus enhance terminal traffic network capacity and safety.

Mining ship deficiency correlations from historical port state control (PSC) inspection data

“…Weng et al used the association rules to determine crucial maritime accident contributory factors and mutual coupled effects on the ship detention incident [11]. Similar studies can be found in [12][13][14][15][16][17][18][19].…”

Research of Vehicle Rear-End Collision Model considering Multiple Factors

“…Note that we can obtain the friction coefficient value with two different working logics, which are empirical-like methods and the nonlinear relevant models. Previous studies suggested that solutions obtained by empirical relevant models may not be very robust, which may further degrade traffic modeling accuracy [28][29][30][31][32]. It is noted that the relationship between the friction coefficient and the typical traffic relevant factors (i.e., road conditions, tire structural parameters, and vehicle speed) is a type of nonlinear function.…”

“…During braking, the deceleration of vehicle changes with time, as shown in Figure 2 [1,30]. Phase I is the reaction phase before performing, and Phase II is the coordination phase between the brake pedal and the vehicle.…”

Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction

“…Note that AIS is a type of self-reporting system originally designed for preventing potential accident, which is a mandatory facility for cargo ships (i.e., ship with gross tonnage larger than 300) [4][5][6][7][8]. Moreover, fishing boats with length longer than 15 m are required to install AIS equipment in the European Union Member States [9][10][11][12].…”