Intelligent Vision-Enabled Detection of Water-Surface Targets for Video Surveillance in Maritime Transportation

Abstract

The timely, automatic, and accurate detection of water-surface targets has received significant attention in intelligent vision-enabled maritime transportation systems. The reliable detection results are also beneficial for water quality monitoring in practical applications. However, the visual image quality is often inevitably degraded due to the poor weather conditions, potentially leading to unsatisfactory target detection results. The degraded images could be restored using state-of-the-art visibility enhancement methods. It is still difficult to generate high-quality detection performance due to the unavoidable loss of details in restored images. To alleviate these limitations, we first investigate the influences of visibility enhancement methods on detection results and then propose a neural network-empowered water-surface target detection framework. A data augmentation strategy, which synthetically simulates the degraded images under different weather conditions, is further presented to promote the generalization and feature representation abilities of our network. The proposed detection performance has the capacity of accurately detecting the water-surface targets under different adverse imaging conditions, e.g., haze, low-lightness, and rain. Experimental results on both synthetic and realistic scenarios have illustrated the effectiveness of the proposed framework in terms of detection accuracy and efficacy.

Strong and Weak Supervision Combined with CLIP for Water Surface Garbage Detection

“…Cai et al [24] applied the CBAM attention module to the YOLOv5 object detection algorithm to develop a target detection model for plastic waste, aiding in the automatic capture of marine debris. Guo et al [25] proposed a neuralnetwork-based water surface garbage object detection framework and further introduced a data augmentation strategy that synthesizes degraded images under different weather conditions to improve the network's generalization and feature representation capabilities. Yang et al [26] proposed a water surface floating garbage motion target big-data detection and recognition method and system based on blockchain technology.…”

A Model to Evaluate the Effectiveness of the Maritime Shipping Risk Mitigation System by Entropy-Based Capability Degradation Analysis

“…This method has been successfully applied to human accident analysis, crew capability building, and crew comprehensive quality evaluation [ 5 , 6 ]. Additionally, in recent years, with the continuous development of science and technology, risk mitigation auxiliary technologies such as video surveillance, risk perception, and information communication in ship risk mitigation have also been widely applied [ 7 , 8 , 9 ]. The introduction of new technologies has improved the level of risk mitigation, but at the same time, it has also brought new security risks.…”

Integrating Shipping Domain Knowledge into Computer Vision Models for Maritime Transportation

“…Then, CV methods, such as mean shift [14], deformable part-based models (DPMs) [15], support vector machine (SVM) [16], and sparse representation [17], are proposed to recognize the specific type of the ships, called ship recognition in the following context for simplicity. Over the past several years, various advanced CV models have been developed to improve the accuracy of recognition results [18].…”

“…Nevertheless, the recognition results obtained from the CV models mentioned above may easily suffer from complicated environments, including water-surface light reflections, multiple moving ships, and severe weather conditions, e.g., hazy, low-light imaging, and rainy [18]. In addition, the training errors naturally and inevitably existing in CV models may exacerbate misjudgment.…”