A lightweight ship target detection model based on improved YOLOv5s algorithm

Zheng, Yuanzhou; Zhang, Yuanfeng; Qian, Long; Zhang, Xinzhu; Diao, Shitong; Liu, Xinyu; Cao, Jingxin; Huang, Haichao

doi:10.1371/journal.pone.0283932

Cited by 35 publications

(10 citation statements)

References 26 publications

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“…YOLOv5 is an improved one-stage target-detection algorithm based on YOLOv3 [26]. Compared with other algorithms in the field of target detection, YOLOv5 has the characteristics of a small model size, fast training and reasoning speed, and flexible use; thus, has been widely used in various fields [27][28][29]. Fig 4 shows the network structure of YOLOv5.…”

Section: Improvement Of the Yolov5mentioning

confidence: 99%

Sugarcane stem node identification algorithm based on improved YOLOv5

Xie,

Li,

Xiao

et al. 2023

PLoS ONE

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Identification of sugarcane stem nodes is generally dependent on high-performance recognition equipment in sugarcane seed pre-cutting machines and inefficient. Accordingly, this study proposes a novel lightweight architecture for the detection of sugarcane stem nodes based on the YOLOv5 framework, named G-YOLOv5s-SS. Firstly, the study removes the CBS and C3 structures at the end of the backbone network to fully utilize shallow-level feature information. This enhances the detection performance of sugarcane stem nodes. Simultaneously, it eliminates the 32 times down-sampled branches in the neck structure and the 20x20 detection heads at the prediction end, reducing model complexity. Secondly, a Ghost lightweight module is introduced to replace the conventional convolution module in the BottleNeck structure, further reducing the model’s complexity. Finally, the study incorporates the SimAM attention mechanism to enhance the extraction of sugarcane stem node features without introducing additional parameters. This improvement aims to enhance recognition accuracy, compensating for any loss in precision due to lightweight modifications. The experimental results showed that the average precision of the improved network for sugarcane stem node identification reached 97.6%, which was 0.6% higher than that of the YOLOv5 baseline network. Meanwhile, a model size of 2.6MB, 1,129,340 parameters, and 7.2G FLOPs, representing respective reductions of 82%, 84%, and 54.4%. Compared with mainstream one-stage target detection algorithms such as YOLOv4-tiny, YOLOv4, YOLOv5n, YOLOv6n, YOLOv6s, YOLOv7-tiny, and YOLOv7, G-YOLOv5s-SS achieved respective average precision improvements of 12.9%, 5.07%, 3.6%, 2.1%, 1.2%, 3%, and 0.4% in sugarcane stem nodes recognition. Meanwhile, the model size was compressed by 88.9%, 98.9%, 33.3%, 72%, 92.9%, 78.8% and 96.3%, respectively. Compared with similar studies, G-YOLOv5s-SS not only enhanced recognition accuracy but also considered model size, demonstrating an overall excellent performance that aligns with the requirements of sugarcane seed pre-cutting machines.

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Section: Improvement Of the Yolov5mentioning

confidence: 99%

Sugarcane stem node identification algorithm based on improved YOLOv5

Xie,

Li,

Xiao

et al. 2023

PLoS ONE

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“…This extension resulted in an impressive mAP of 63.4 on the COCO dataset. Zheng et al (2023) proposed a lightweight YOLOv5 model to detect ships, and the detection accuracy increased by 3.4%. Lou et al (2023) proposed a small-size object detection algorithm based on YOLOv8 model for special scenes, which improved the detection accuracy of small-size objects.…”

Section: Introductionmentioning

confidence: 99%

An improved lightweight object detection algorithm for YOLOv5

Luo,

Wei,

Wang

et al. 2024

PeerJ Computer Science

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Object detection based on deep learning has made great progress in the past decade and has been widely used in various fields of daily life. Model lightweighting is the core of deploying target detection models on mobile or edge devices. Lightweight models have fewer parameters and lower computational costs, but are often accompanied by lower detection accuracy. Based on YOLOv5s, this article proposes an improved lightweight target detection model, which can achieve higher detection accuracy with smaller parameters. Firstly, utilizing the lightweight feature of the Ghost module, we integrated it into the C3 structure and replaced some of the C3 modules after the upsample layer on the neck network, thereby reducing the number of model parameters and expediting the model’s inference process. Secondly, the coordinate attention (CA) mechanism was added to the neck to enhance the model’s ability to pay attention to relevant information and improved detection accuracy. Finally, a more efficient Simplified Spatial Pyramid Pooling—Fast (SimSPPF) module was designed to enhance the stability of the model and shorten the training time of the model. In order to verify the effectiveness of the improved model, experiments were conducted using three datasets with different features. Experimental results show that the number of parameters of our model is significantly reduced by 28% compared with the original model, and mean average precision (mAP) is increased by 3.1%, 1.1% and 1.8% respectively. The model also performs better in terms of accuracy compared to existing lightweight state-of-the-art models. On three datasets with different features, mAP of the proposed model achieved 87.2%, 77.8% and 92.3%, which is better than YOLOv7tiny (81.4%, 77.7%, 90.3%), YOLOv8n (84.7%, 77.7%, 90.6%) and other advanced models. When achieving the decreased number of parameters, the improved model can successfully increase mAP, providing great reference for deploying the model on mobile or edge devices.

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“…The employment of robots in several domains, including environmental surveillance [40], vehicle maneuvering [41], driver [42] and environment identification [43], energy recharging and consumption [44], subaquatic inquiry [45], and search and rescue missions [46], has generated significant attention in recent times. The optimization of path planning is a critical factor in enabling robots to navigate complex environments [47], thus guaranteeing their secure and effective arrival at predetermined destinations [48]. The method of path planning involves the application of intelligence algorithms to determine the optimal course for a robot between two specified points [49], while also avoiding obstacles and limiting the risk of collisions [50].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Robot Path Planning Through a Twin-Reinforced Chimp Optimization Algorithm and Evolutionary Programming Algorithm

Zhang,

Zhang

2024

IEEE Access

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The importance of efficient path planning (PP) cannot be overstated in the domain of robots, as it involves the utilization of intelligent algorithms to determine the optimal trajectory for robot to navigate between two given points. The main target of PP is to determine potential trajectories for robot operating in a complex environment containing various obstacles. The implementation of these movements should facilitate robot in traversing a path without encountering any collisions, starting from its initial location and reaching the intended destination. In order to address the challenges associated with robot PP, this study applies the chimp optimization algorithm (CHOA) as a local searching (LS) technique and the evolutionary programming algorithm (EPA) to enhance the potential route discovered via a collection of LSs. In order to address CHOA's tendency to converge to local minima, a new updating technique called twin-reinforced (TR) is developed. In order to assess the effectiveness of the TRCHOA, we conducted a comparative analysis with other widely used meta-heuristic algorithms that are typically employed for solving robot PP problems. Additionally, we included the conventional probabilistic roadmap method (PRM) in our evaluation. We evaluated the planning performances of these algorithms on a standardized set of benchmark problems. Our findings indicate that the TRCHOA outperforms the other algorithms in terms of its planning performance. The evaluation of planning effectiveness encompasses several key criteria, namely path length, consistency of scheduled paths, time complexity, and rate of success. The experiments conducted in this study provide evidence of the statistically significant value of the enhancements obtained through the implementation of the proposed method. The findings derived from the TRCHOA provide compelling evidence of its capacity to accurately determine the most optimal route within the specified test map.INDEX TERMS Chimp optimization algorithm; evolutionary programming; path planning; robot; twinreinforced technique.

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A lightweight ship target detection model based on improved YOLOv5s algorithm

Cited by 35 publications

References 26 publications

Sugarcane stem node identification algorithm based on improved YOLOv5

Sugarcane stem node identification algorithm based on improved YOLOv5

An improved lightweight object detection algorithm for YOLOv5

Enhancing Robot Path Planning Through a Twin-Reinforced Chimp Optimization Algorithm and Evolutionary Programming Algorithm

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