Bridge crack detection based on improved single shot multi-box detector

Lu, Guanlin; He, Xiaohui; Wang, Qiang; Shao, Faming; Wang, Jinkang; Jiang, Qunyan

doi:10.1371/journal.pone.0275538

Cited by 11 publications

(5 citation statements)

References 36 publications

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“…Second, the feature mapping relationships of conv4_3, conv7_2, conv8_2, conv9_2, conv10_2, and conv11 are combined to form a multiscale feature extraction layer in the SSD. Finally, a 3×3 convolution is used to calculate the output feature graphs of the detection layer one by one to obtain the confidence [20] . Fig.…”

Section: Taxonomy a Single Shot Multibox Detector (Ssd)mentioning

confidence: 99%

Object Detection Model: An Enhanced SSD

Sharma

2024

IJRASET

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Object Detection plays a significant role in today’s era. Object detection plays a crucial role in various applications such as surveillance, medical images, and autonomous vehicles. The primary goal of object detection is to accurately detect the boundaries of objects of interest and classify those objects into predefined categories. In this research paper, we enhanced an object detection technique i.e. Single shot multibox detector (SSD) which is one of the top object detection technique in both aspect accuracy and speed. SSD is an object detection model that predicts object bounding boxes and class probabilities in a single forward pass. It uses multiple feature maps at different scales to cater to various object sizes and aspect ratios.The main contribution of this research is to enhance the accuracy, recall time, precision time and mean average time (mAP). The performance of this object detection technique is improved as the number of feature maps is increases, improved backbone, and enhanced activation functions, additional layers for enhanced detection and loss function modifications. An accuracy of 0.75 indicates that the model correctly predicts the class label for approximately 75% of all objects in the dataset. The enhanced model showed these results: a precision score of 0.75 implies that 75% of the objects identified by the model as positive (detected objects) are indeed true positives. A recall of 0.73 indicates that the model successfully identifies and detects 73% of all positive instances in the dataset. A mAP of 75.25 signifies the average precision across all classes, computed at different IOU thresholds (e.g., 0.5, 0.75).

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Section: Taxonomy a Single Shot Multibox Detector (Ssd)mentioning

confidence: 99%

Object Detection Model: An Enhanced SSD

Sharma

2024

IJRASET

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“…It worth noting that object detection is different from the deep learning based automatic modulation classification [6,7], specific emitter identification [8][9][10] and malware traffic classification [11,12]. Object detection algorithms are divided into two categories: two-stage detection [13][14][15][16] and single detection [17][18][19]. Although the two-stage detection accuracy is higher, the detection speed is slower in real-time detection.…”

Section: Introductionmentioning

confidence: 99%

Small target disease detection based on YOLOv5 framework for intelligent bridges

Zhang,

Xiong,

Jiang

et al. 2024

Peer-to-Peer Netw. Appl.

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This paper proposes a small target disease detection method using YOLOv5 framework for detecting small apparent diseases on intelligent bridges, aiming to address the problem of missed and false detection. To enhance the detection of small apparent diseases, a layer for detecting small objects is added to the YOLOv5 model. Additionally, an ECA attention mechanism module is embedded in the feature enhancement network to improve the extraction of disease features. To validate the effectiveness of the proposed algorithm, a dataset of 996 bridges with apparent diseases such as corrosion, rebar, speckle, hole and spall was established and trained after manual annotation and data augmentation. The experiment showed that the proposed algorithm achieves a mAP of 87.91%. Compared to the original YOLOv5 model, the proposed algorithm improved the mAP on the bridge apparent disease dataset by 1.97%. This algorithm accurately detects small apparent diseases on bridges and effectively reduces missed detection.

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“…In addition, the proposed loss function of Focal Loss [18] solves the problem of positive and negative sample imbalance, which in turn makes up for the lack of detection accuracy of the single-stage detection algorithm. Lu et al [19] used an improved SSD network for crack detection. The detection accuracy is improved while meeting the demand for real-time detection.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Lightweight Network for Road Damage Detection Based on Deep Learning

Luo

et al. 2023

Electronics

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Achieving accurate and efficient detection of road damage in complex scenes has always been a challenging task. In this paper, an enhanced lightweight network, E-EfficientDet, is proposed. Firstly, a feature extraction enhancement module (FEEM) is designed to increase the receptive field and improve the feature expression capability of the network, which can extract richer multi-scale feature information. Secondly, to promote the reuse of feature information between different layers in the network and take full advantage of multi-scale context information, four pyramid modules with different structures are designed based on the idea of semi-dense connection, among which the bidirectional feature pyramid network with longitudinal connection (LC-BiFPN) is more suitable for road damage detection. Finally, to meet the road damage detection tasks under different hardware resource constraints, the E-EfficientDet-D0~D2 networks are proposed in this paper based on the compound scaling strategy. Experimental results show that the detection accuracy of E-EfficientDet-D0 improves by 2.41% compared with the original EfficientDet-D0 on the publicly available road damage dataset and outperforms other networks such as YOLOv5s, YOLOv7-tiny, YOLOv4-tiny, Faster R-CNN, and SSD. Meanwhile, the detection speed of EfficientDet-D0 can reach 27.0 FPS, which meets the demand for real-time detection, and the model size is only 32.31 MB, which is suitable for deployment in mobile devices such as unmanned inspection carts, UAVs, and smartphones. In addition, the detection accuracy of E-EfficientDet-D2 can reach 57.51%, which is 4.39% higher than E-EfficientDet-D0, and the model size is 61.78 MB, which is suitable for practical application scenarios that require higher detection accuracy and better hardware performance.

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Bridge crack detection based on improved single shot multi-box detector

Cited by 11 publications

References 36 publications

Object Detection Model: An Enhanced SSD

Object Detection Model: An Enhanced SSD

Small target disease detection based on YOLOv5 framework for intelligent bridges

An Enhanced Lightweight Network for Road Damage Detection Based on Deep Learning

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