Multi-AUV Collaborative Target Recognition Based on Transfer-Reinforcement Learning

Abstract

Due to the existence of unfavorable factors such as turbid water quality and target occlusion, it is difficult to obtain valid data of target features. Due to the repeated calculation of similar data, the real-time performance of the algorithm is poor. In view of the above problems, this paper proposes a multi-AUV collaborative target recognition method based on transfer-reinforcement learning. The features of the target information which is collected by multi-AUV are fused based on wavelet transformation and affine invariance. The similarity of features is calculated by Mahalanobis distance and the learning model is selected autonomously based on the similarity threshold. Based on the Q-learning reinforcement learning model, the target information under the interference environment is trained intensively, and the effective features are extracted and stored in the source domain, which can reduce the impact of the environmental interference on the target recognition. The feature transfer learning model based on deep confidence network transfers the feature data of the source domain to the target domain, reducing the repeated calculation of similar data, and then ensuring the real-time performance of the algorithm. Simulation experiments are conducted in the SUN dataset under five underwater environments (turbid water, target occlusion, insufficient light, complex background, and overlapping targets), and the results demonstrate that the proposed model achieves better performance.

Underwater Distortion Target Recognition Network (UDTRNet) via Enhanced Image Features

“…For the problems of underwater environment interference and algorithm real time, Cai et al [14] proposed a collaborative multi-AUV target recognition method based on migration reinforcement learning. Zhang et al [15] proposed a semantic spatial fusion network (SSFNet) to bridge the gap between low-level and high-level features.…”

Glass Refraction Distortion Object Detection via Abstract Features

“…In the process of collecting object information, in order to avoid the detection of enemy personnel, the algorithm is required to have good real-time, with fast search and object identification capabilities. Effective training of the model is an important prerequisite for detection algorithms, and the larger the number of samples and the stronger the training model, the better the detection effect [ 4 ]. But during the process of translucent glass detection, uncontrollable unknown factors such as uneven glass thickness or bending, scattering, reflection cause problems such as distortion and deformation, blurred or missing feature information that is difficult to predict in the collected sample images, making it difficult to label them accurately as well as to object training.…”

A Marine Organism Detection Framework Based on the Joint Optimization of Image Enhancement and Object Detection

“…The rapid development of underwater observation technology provides underwater optical vision with very broad application prospects. As a typical application of underwater optical vision, underwater visual target detection plays an increasingly important role in underwater security [1][2][3][4], marine exploration [5,6], fish farming [7] and marine ecology [8,9]. Therefore, the achievement of underwater autonomous operation through visual target detection completion by use of underwater optical images has become a research hotspot in the field of computer vision [1].…”