Event cameras are bio-inspired vision sensors with a high dynamic range (140 dB for event cameras vs. 60 dB for traditional cameras) and can be used to tackle the image degradation problem under extremely low-illumination scenarios, which is still not well-explored yet. In this paper, we propose a joint framework to compose the underexposed frames and event streams captured by the event camera to reconstruct clear images with detailed textures under almost dark conditions. A residual fusion module is proposed to reduce the domain gap between event streams and frames by using the residuals of both modalities. A multi-level reconstruction loss based on the variability of the contrast distribution is proposed to reduce the perceptual errors of the output image. In addition, we construct the first real-world low-illumination image enhancement dataset (mainly under 2 lux illumination scenes), named LIE, containing event streams and frames collected under indoor and outdoor low-light scenarios together with the ground truth clear images. Experimental results on our LIE dataset demonstrate that our proposed method could achieve significant improvements compared with existing methods.

The underwater environment is still unknown for humans, so the high definition camera is an important tool for data acquisition at short distances underwater. Due to insufficient power, the image data collected by underwater submersible devices cannot be analyzed in real time. Based on the characteristics of Field-Programmable Gate Array (FPGA), low power consumption, strong computing capability, and high flexibility, we design an embedded FPGA image recognition system on Convolutional Neural Network (CNN). By using two technologies of FPGA, parallelism and pipeline, the parallelization of multi-depth convolution operations is realized. In the experimental phase, we collect and segment the images from underwater video recorded by the submersible. Next, we join the tags with the images to build the training set. The test results show that the proposed FPGA system achieves the same accuracy as the workstation, and we get a frame rate at 25 FPS with the resolution of 1920 × 1080. This meets our needs for underwater identification tasks.

Target search for moving and invisible objects has always been considered a challenge, as the floating objects drift with the flows. This study focuses on target search by multiple autonomous underwater vehicles (AUV) and investigates a multi-agent target search method (MATSMI) for moving and invisible objects. In the MATSMI algorithm, based on the multi-agent deep deterministic policy gradient (MADDPG) method, we add spatial and temporal information to the reinforcement learning state and set up specialized rewards in conjunction with a maritime target search scenario. Additionally, we construct a simulation environment to simulate a multi-AUV search for the floating object. The simulation results show that the MATSMI method has about 20% higher search success rate and about 70 steps shorter search time than the traditional search method. In addition, the MATSMI method converges faster than the MADDPG method. This paper provides a novel and effective method for solving the maritime target search problem.

With the rapid development of sensor networks, big marine data arises. To efficiently use these data to predict thermoclines, we propose a machine learning approach. We firstly focus on analyzing how temperature, salinity, and geographic location features affect the formation of thermocline. Then, an improved model based on entropy value method for the thermocline selection is demonstrated. The experiments adopt BOA Argo data sets and the experimental results show that our novel model can predict thermoclines and related data effectively.