Underwater object detection algorithm based on feature enhancement and progressive dynamic aggregation strategy

HUA, XIA; Cui, Xiaopeng; Xu, Xinghua; Qiu, Shaohua; Liang, Yongtu; Bao, Xianqiang; LI, ZHONG

doi:10.1016/j.patcog.2023.109511

Cited by 19 publications

(6 citation statements)

References 10 publications

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“…Hua et al, [14] have introduced a new type of subaquatic object detector that employs YOLOv5s. At first, a module was created to improve or decrease various hierarchical properties in a controlled way and reduce the interference of acoustic signals from intricate underwater environments during feature fusion.…”

Section: Related Workmentioning

confidence: 99%

“…The HLAST-ACNet method is compared to YOLOv5 [14], ResNet [18], and YOLOv8 [19] using comparative tests to highlight its superiority. Figure 7 Research suggests that when it comes to identifying and locating small or camouflaged objects in complicated underwater settings, current methods often fall short due to their inadequate feature representation.…”

Section: A) Experimental Setupmentioning

confidence: 99%

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HLASwin-T-ACoat-Net Based Underwater Object Detection

Manimurugan,

Narmatha,

Aborokbah

et al. 2024

IEEE Access

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Due to the limited light penetration in underwater environments, sonar equipment plays a crucial role in various commercial and military operations. However, underwater images often suffer from degradation due to scattering and absorption phenomena, resulting in poor visibility of submerged objects. To address this challenge, image enhancement techniques are essential for enhancing the appearance and visibility of underwater objects. This research proposes a novel approach called HLAST-ACNet, which combines the advantages of a hybrid Local Acuity Swin Transformer and an Adapted Coat-Net for Underwater Object Detection (UOD). The HLASwin-T-ACoat-Net leverages Contrast Limited Adaptive Histogram Equalization (CLAHE) to increase the quality of images. Additionally, it incorporates a path aggregation network to integrate deep and shallow feature maps and utilizes online complicated example mining to improve training efficiency. Furthermore, the algorithm improves Region of Interest (ROI) pooling by introducing ROI alignment, which mitigates quantization errors and enhances object detection accuracy. Compared to existing algorithms, the algorithms based on HLASTACNet demonstrate significant improvements in the URPC2018 and OUC datasets, achieving precision rates of 91.25% and 92.36%, respectively. The research model has a higher computational complexity than four existing methods, as evidenced by its GFLOPs, per-image processing time with a speed of 20ms, and the FPS measures for average processed frames per second reaching 2.28s. The research model effectively addressed the challenges and false detection with varying sizes of objects in complicated underwater environments.

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Section: Related Workmentioning

confidence: 99%

Section: A) Experimental Setupmentioning

confidence: 99%

HLASwin-T-ACoat-Net Based Underwater Object Detection

Manimurugan,

Narmatha,

Aborokbah

et al. 2024

IEEE Access

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show abstract

“…This paper presents a solution to address the cost and real-time limitations of underwater detection devices by introducing a compact network with precise and fast detection capabilities. The underwater environment is characterized by its intricacy and constant change, which lead to environmental noise that can significantly degrade the performance of underwater detection devices [5]. In most captured underwater images, a predominant color palette of blue-green hues dominates, creating a major obstacle in effectively discerning underwater targets from the background.…”

Section: Introductionmentioning

confidence: 99%

YOLOv7-CHS: An Emerging Model for Underwater Object Detection

Zhao,

Yun,

Yuan

et al. 2023

JMSE

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Underwater target detection plays a crucial role in marine environmental monitoring and early warning systems. It involves utilizing optical images acquired from underwater imaging devices to locate and identify aquatic organisms in challenging environments. However, the color deviation and low illumination in these images, caused by harsh working conditions, pose significant challenges to an effective target detection. Moreover, the detection of numerous small or tiny aquatic targets becomes even more demanding, considering the limited storage and computing power of detection devices. To address these problems, we propose the YOLOv7-CHS model for underwater target detection, which introduces several innovative approaches. Firstly, we replace efficient layer aggregation networks (ELAN) with the high-order spatial interaction (HOSI) module as the backbone of the model. This change reduces the model size while preserving accuracy. Secondly, we integrate the contextual transformer (CT) module into the head of the model, which combines static and dynamic contextual representations to effectively improve the model’s ability to detect small targets. Lastly, we incorporate the simple parameter-free attention (SPFA) module at the head of the detection network, implementing a combined channel-domain and spatial-domain attention mechanism. This integration significantly improves the representation capabilities of the network. To validate the implications of our model, we conduct a series of experiments. The results demonstrate that our proposed model achieves higher mean average precision (mAP) values on the Starfish and DUO datasets compared to the original YOLOv7, with improvements of 4.5% and 4.2%, respectively. Additionally, our model achieves a real-time detection speed of 32 frames per second (FPS). Furthermore, the floating point operations (FLOPs) of our model are 62.9 G smaller than those of YOLOv7, facilitating the deployment of the model. Its innovative design and experimental results highlight its effectiveness in addressing the challenges associated with underwater object detection.

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“…The network structure of YOLOv7 is a redesigned and lightweight model [4], which is better suited for deployment on an edge gateway system. To address diverse real-time OD scenarios within the context of LPR systems, the training model was directly deployed on the edge gateway [9], and the LPR was directly performed on the edge gateway. In times of idleness, the data are automatically transferred to the cloud-based device.…”

Section: Introductionmentioning

confidence: 99%

A Light Vehicle License-Plate-Recognition System Based on Hybrid Edge–Cloud Computing

Leng,

Chen,

Zhao

et al. 2023

Sensors

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With the world moving towards low-carbon and environmentally friendly development, the rapid growth of new-energy vehicles is evident. The utilization of deep-learning-based license-plate-recognition (LPR) algorithms has become widespread. However, existing LPR systems have difficulty achieving timely, effective, and energy-saving recognition due to their inherent limitations such as high latency and energy consumption. An innovative Edge–LPR system that leverages edge computing and lightweight network models is proposed in this paper. With the help of this technology, the excessive reliance on the computational capacity and the uneven implementation of resources of cloud computing can be successfully mitigated. The system is specifically a simple LPR. Channel pruning was used to reconstruct the backbone layer, reduce the network model parameters, and effectively reduce the GPU resource consumption. By utilizing the computing resources of the Intel second-generation computing stick, the network models were deployed on edge gateways to detect license plates directly. The reliability and effectiveness of the Edge–LPR system were validated through the experimental analysis of the CCPD standard dataset and real-time monitoring dataset from charging stations. The experimental results from the CCPD common dataset demonstrated that the network’s total number of parameters was only 0.606 MB, with an impressive accuracy rate of 97%.

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Underwater object detection algorithm based on feature enhancement and progressive dynamic aggregation strategy

Cited by 19 publications

References 10 publications

HLASwin-T-ACoat-Net Based Underwater Object Detection

HLASwin-T-ACoat-Net Based Underwater Object Detection

YOLOv7-CHS: An Emerging Model for Underwater Object Detection

A Light Vehicle License-Plate-Recognition System Based on Hybrid Edge–Cloud Computing

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