Ship Target Detection Algorithm Based on Improved YOLOv5

Zhou, Junchi; Jiang, Ping; Zou, Airu; Chen, Xinglin; Hu, Wei

doi:10.3390/jmse9080908

Cited by 55 publications

(33 citation statements)

References 14 publications

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“…Furthermore, to solve the erroneous computation of non-overlapping B_Boxes, YOLOv5 choose GIoU loss as the B_Boxes regression loss function, which is defined by Eq. (11).…”

Section: Non-maximum Suppression (Nms) Techniquementioning

confidence: 99%

“…Although, the existing algorithms have improved the performance with emerging deep learning technology. The addition of the one-stage detector-based YOLO (You Look Only Once) series version has improved the performance in detection rate [11]. However, these existing algorithms still have room for improvement in the detection rate and performance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Vehicle Detection Based on Improved You Look Only Once

Kumar¹,

Jailia²,

Varshney³

et al. 2023

Computers, Materials &Amp; Continua

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Vehicle detection is still challenging for intelligent transportation systems (ITS) to achieve satisfactory performance. The existing methods based on one stage and two-stage have intrinsic weakness in obtaining high vehicle detection performance. Due to advancements in detection technology, deep learning-based methods for vehicle detection have become more popular because of their higher detection accuracy and speed than the existing algorithms. This paper presents a robust vehicle detection technique based on Improved You Look Only Once (RVD-YOLOv5) to enhance vehicle detection accuracy. The proposed method works in three phases; in the first phase, the K-means algorithm performs data clustering on datasets to generate the classes of the objects. Subsequently, in the second phase, the YOLOv5 is applied to create the bounding box, and the Non-Maximum Suppression (NMS) technique is used to eliminate the overlapping of the bounding boxes of the vehicle. Then, the loss function CIoU is employed to obtain the accurate regression bounding box of the vehicle in the third phase. The simulation results show that the proposed method achieves better results when compared with other state-of-art techniques, namely Lightweight Dilated Convolutional Neural Network (LD-CNN), Single Shot Detector (SSD), YOLOv3 and YOLOv4 on the performance metric like precision, recall, mAP and F1-Score. The simulation and analysis are carried out on PASCAL VOC 2007, 2012 and MS COCO 2017 datasets to obtain better performance for vehicle detection. Finally, the RVD-YOLOv5 obtains the results with an mAP of 98.6% and Precision, Recall, and F1-Score are 98%, 96.2% and 97.09%, respectively.

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“…Furthermore, to solve the erroneous computation of non-overlapping B_Boxes, YOLOv5 choose GIoU loss as the B_Boxes regression loss function, which is defined by Eq. (11).…”

Section: Non-maximum Suppression (Nms) Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Vehicle Detection Based on Improved You Look Only Once

Kumar¹,

Jailia²,

Varshney³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Numerous scholars across the world have conducted extensive research on target object recognition technology [10][11][12][13]. In the field of fruit crop detection in natural environments, feature extraction and recognition have predominately targeted tomato [14,15], apple [16][17][18], cucumber [19,20], strawberry [21], sugarcane [22], pineapple [23], and various other fruits.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Detection Algorithm of Kiwifruit Based on Improved YOLOX-S

Zhou

Zou

et al. 2022

Agriculture

Self Cite

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Considering the high requirements of current kiwifruit picking recognition systems for mobile devices, including the small number of available features for image targets and small-scale aggregation, an enhanced YOLOX-S target detection algorithm for kiwifruit picking robots is proposed in this study. This involved designing a new multi-scale feature integration structure in which, with the aim of providing a small and lightweight model, the feature maps used for detecting large targets in the YOLOX model are eliminated, the feature map of small targets is sampled through the nearest neighbor values, the superficial features are spliced with the final features, the gradient of the SiLU activation function is perturbed, and the loss function at the output is optimized. The experimental results show that, compared with the original YOLOX-S, the enhanced model improved the detection average precision (AP) of kiwifruit images by 6.52%, reduced the number of model parameters by 44.8%, and improved the model detection speed by 63.9%. Hence, with its outstanding effectiveness and relatively light weight, the proposed model is capable of effectively providing data support for the 3D positioning and automated picking of kiwifruit. It may also successfully provide solutions in similar fields related to small target detection.

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“…It is necessary to further improve the detection of small targets. To improve the target detection ability, Zhou et al (2021) improved the YOLOv5s algorithm by re-clustering the initial frame of the target by improving Kmeans, expanding the receptive field area at the output, and optimizing the loss function.…”

Section: Introductionmentioning

confidence: 99%

Research on target detection of Engraulis japonicus purse seine based on improved model of YOLOv5

et al. 2022

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The refined monitoring and identification of fishing operations by fishing vessels is of great significance and value to fishing vessels. In order to solve the problem of inaccurate statistics of current Engraulis japonicus fishing quota and classification, this paper proposes an improved identification algorithm based on YOLOv5. This method introduces the SENet attention mechanism into the YOLOv5 backbone network structure, integrates the target information in different periods of fishing operations, reduces the interference of complex backgrounds, improves the precision of model detection, and ensures real-time detection efficiency. The artificially shot Engraulis japonicus videos are used as the dataset of the study, and the video is converted into a picture format to realize pre-labeling and processing. The 5550 images are divided into the training set, validation set, and test set according to 8:1:1. In order to verify the validity of the data, a set of in the control experiment, the YOLOv5 backbone network was replaced by MobileNetV2 and the SENet attention mechanism was introduced, and four models were implemented for comparison. The experimental results show that the research algorithm can obtain a mean average precision (mAP) of 99.3%, a precision of 98.9%, and a recall of 98.7%, which are improved by 1.4%, 1.7% and 2.5% respectively compared with the original model. The experimental results match the expectations. According to the statistics of some categories, the Kalman filter and the Hungarian matching method are used to count the main categories of fishing baskets, and an accuracy of 96.5% can be obtained. The threshold method for fishing nets and processing vessels can obtain 85.8% and 75% accuracy. These results shows that this target detection research can provide new ideas for job identification of Engraulis japonicus and provide auxiliary means for job statistics.

show abstract

Ship Target Detection Algorithm Based on Improved YOLOv5

Cited by 55 publications

References 14 publications

Robust Vehicle Detection Based on Improved You Look Only Once

Robust Vehicle Detection Based on Improved You Look Only Once

Lightweight Detection Algorithm of Kiwifruit Based on Improved YOLOX-S

Research on target detection of Engraulis japonicus purse seine based on improved model of YOLOv5

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