Object Detection for Construction Waste Based on an Improved YOLOv5 Model

Zhou, Qinghui; Liu, Haoshi; Qiu, Yuhang; Zheng, Wuchao

doi:10.3390/su15010681

Cited by 26 publications

(7 citation statements)

References 26 publications

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“…By introducing SimSPPF to fuse the local and global features of insulators and their defects, the semantic information of feature graphs can be enriched. In addition, compared with SPPF network, SimSPPF can not only effectively solve the problem of image distortion and repeated extraction of related features, but also significantly improve the speed of model detection by introducing ConvBNReLU module [7] .…”

Section: Shufflenetv2-ghostconv-conv-simsppf Feature Extraction Modulementioning

confidence: 99%

Insulator and Its Defect Detection Based on Lightweight YOLOv5s

Shi

2024

Frontiers in Artificial Intelligence and Applications

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In order to achieve precise and real-time detection of insulators and their defects by drones, a lightweight YOLOv5s based insulator and its defect detection model YOLOv5s-SGCS-GB-4PH was designed. Firstly, the lightweight network Shufflenetv2-Ghostconv-Conv-SimSPPF (SGCS) was used as the backbone network of YOLOv5s, effectively reducing the number of parameters and complexity of the model; Secondly, introducing GSConv into the neck network to improve model efficiency; Then, a bidirectional feature pyramid network and an additional small target detection head are used to improve the model’s ability to perceive insulators and their defects; Finally, SioU-VF loss function is selected to obtain better target location and improve model accuracy. The experiment shows that the YOLOv5s-SGCS-GB-4PH model reduces the parameter and computational complexity by 34.3% and 48.1% respectively on self-made insulator data, and improves the detection accuracy by 3.6%, achieving a balance between lightweight and detection accuracy. It is more suitable for deployment on unmanned aerial vehicles for real-time detection of insulators and their defects.

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Section: Shufflenetv2-ghostconv-conv-simsppf Feature Extraction Modulementioning

confidence: 99%

Insulator and Its Defect Detection Based on Lightweight YOLOv5s

Shi

2024

Frontiers in Artificial Intelligence and Applications

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“…The improved algorithm increased the accuracy of detecting surface defects on printed circuit boards by 3.50% [29]. Zhou et al improved the YOLOv5 target detection by using a Convolutional Block Attention Module (CBAM), which achieved a significant increase in accuracy compared to the original model [30].…”

Section: Related Work 21 Application Of Yolo In Mask-wearing Detectionmentioning

confidence: 99%

Small target detection algorithm based on attention mechanism and data augmentation

Wang,

Liu,

et al. 2024

SIViP

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The detection of masks is of great significance to the prevention of occupational diseases such as infectious diseases and dust diseases. For the problems of small target size, large number of targets, and mutual occlusion in mask-wearing detection, a mask-wearing detection algorithm based on improved YOLOv5s is proposed in present work. First, the ultra-lightweight attention mechanism module ECA was introduced in the Neck layer to improve the accuracy of the model. Second, the influence of different loss functions (GIoU, CIoU, and DIoU) on the improved model was explored, and CIoU was determined as the loss function of the improved model. Besides, the improved model adopted the label smoothing method at the data processing stage, which effectively improved the generalization ability of the model and reduced the risk of overfitting. Finally, the influence of data augmentation methods (Mosaic and Mixup) on model performance was discussed, and the optimal weight of data augmentation was determined. The proposed model was tested on the verification set and the mean precision (mAP), precision, and recall are 92.1%, 90.3%, and 87.4%, respectively. The mAP of the improved algorithm is 4.4% higher than that of the original algorithm.

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“…The decision to use the SimSPPF module was based on the observed advantages of SimSPPF over SPPF, such as the reduced computational complexity and higher FPS while maintaining good object detection accuracy. The equations are shown in Equations ( 1)-( 5) [33].…”

Section: Simsppf (Simplified Sppf)mentioning

confidence: 99%

Improved YOLOv5-Based Lightweight Object Detection Algorithm for People with Visual Impairment to Detect Buses

2023

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Object detection is crucial for individuals with visual impairment, especially when waiting for a bus. In this study, we propose a lightweight and highly accurate bus detection model based on an improved version of the YOLOv5 model. We propose integrating the GhostConv and C3Ghost Modules into the YOLOv5 network to reduce the number of parameters and floating-point operations per second (FLOPs), ensuring detection accuracy while reducing the model parameters. Following that, we added the SimSPPF module to replace the SPPF in the YOLOv5 backbone for increased computational efficiency and accurate object detection capabilities. Finally, we developed a Slim scale detection model by modifying the original YOLOv5 structure in order to make the model more efficient and faster, which is critical for real-time object detection applications. According to the experimental results, the Improved-YOLOv5 outperforms the original YOLOv5 in terms of the precision, recall, and mAP@0.5. Further analysis of the model complexity reveals that the Improved-YOLOv5 is more efficient due to fewer FLOPS, with fewer parameters, less memory usage, and faster inference time capabilities. The proposed model is smaller and more feasible to implement in resource-constrained mobile devices and a promising option for bus detection systems.

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Object Detection for Construction Waste Based on an Improved YOLOv5 Model

Cited by 26 publications

References 26 publications

Insulator and Its Defect Detection Based on Lightweight YOLOv5s

Insulator and Its Defect Detection Based on Lightweight YOLOv5s

Small target detection algorithm based on attention mechanism and data augmentation

Improved YOLOv5-Based Lightweight Object Detection Algorithm for People with Visual Impairment to Detect Buses

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