Passive ship detection and classification using hybrid cepstrums and deep compound autoencoders

Kamalipour, Maryam; Agahi, Hamed; Khishe, Mohammad; Mahmoodzadeh, Azar

doi:10.1007/s00521-022-08075-7

Cited by 10 publications

(1 citation statement)

References 89 publications

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“…Yutong et al 35 presented a hierarchical deep learning framework combining a CNN, Extreme learning machine, and a fuzzy slime mould optimizer for real-time sonar image recognition, demonstrating excellent detection accuracy. Kamalipour et al 36 proposed a novel deep convolutional-recurrent autoencoder for robust passive ship detection and classification in complex underwater acoustic environments. Tian et al 37 propose a Radial basis function neural network enhanced by the chimp optimization algorithm for improved underwater image detection and recognition.…”

Section: Related Workmentioning

confidence: 99%

A streamlined approach for intelligent ship object detection using EL-YOLO algorithm

Yang,

Solihin,

Ardiyanto

et al. 2024

Sci Rep

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Maritime objects frequently exhibit low-quality and insufficient feature information, particularly in complex maritime environments characterized by challenges such as small objects, waves, and reflections. This situation poses significant challenges to the development of reliable object detection including the strategies of loss function and the feature understanding capabilities in common YOLOv8 (You Only Look Once) detectors. Furthermore, the widespread adoption and unmanned operation of intelligent ships have generated increasing demands on the computational efficiency and cost of object detection hardware, necessitating the development of more lightweight network architectures. This study proposes the EL-YOLO (Efficient Lightweight You Only Look Once) algorithm based on YOLOv8, designed specifically for intelligent ship object detection. EL-YOLO incorporates novel features, including adequate wise IoU (AWIoU) for improved bounding box regression, shortcut multi-fuse neck (SMFN) for a comprehensive analysis of features, and greedy-driven filter pruning (GDFP) to achieve a streamlined and lightweight network design. The findings of this study demonstrate notable advancements in both detection accuracy and lightweight characteristics across diverse maritime scenarios. EL-YOLO exhibits superior performance in intelligent ship object detection using RGB cameras, showcasing a significant improvement compared to standard YOLOv8 models.

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Section: Related Workmentioning

confidence: 99%