GBH-YOLOv5: Ghost Convolution with BottleneckCSP and Tiny Target Prediction Head Incorporating YOLOv5 for PV Panel Defect Detection

Li, Longlong; Wang, Zhifeng; Zhang, Tingting

doi:10.3390/electronics12030561

Cited by 59 publications

(29 citation statements)

References 53 publications

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“…Since the proposal of YOLOv5, numerous scholars have made various improvements to enhance its model performance. Li et al [ 49 ] proposed GBH-YOLOv5 for PV panel defect detection. The model introduces the BottleneckCSP module, which adds a tiny target prediction head to enhance the detection capability of smaller targets, and employs Ghost convolution to further reduce the inference time.…”

Section: Related Workmentioning

confidence: 99%

An Improved YOLOv5-Based Underwater Object-Detection Framework

Jian

Zhang

Zhou

et al. 2023

Sensors

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To date, general-purpose object-detection methods have achieved a great deal. However, challenges such as degraded image quality, complex backgrounds, and the detection of marine organisms at different scales arise when identifying underwater organisms. To solve such problems and further improve the accuracy of relevant models, this study proposes a marine biological object-detection architecture based on an improved YOLOv5 framework. First, the backbone framework of Real-Time Models for object Detection (RTMDet) is introduced. The core module, Cross-Stage Partial Layer (CSPLayer), includes a large convolution kernel, which allows the detection network to precisely capture contextual information more comprehensively. Furthermore, a common convolution layer is added to the stem layer, to extract more valuable information from the images efficiently. Then, the BoT3 module with the multi-head self-attention (MHSA) mechanism is added into the neck module of YOLOv5, such that the detection network has a better effect in scenes with dense targets and the detection accuracy is further improved. The introduction of the BoT3 module represents a key innovation of this paper. Finally, union dataset augmentation (UDA) is performed on the training set using the Minimal Color Loss and Locally Adaptive Contrast Enhancement (MLLE) image augmentation method, and the result is used as the input to the improved YOLOv5 framework. Experiments on the underwater datasets URPC2019 and URPC2020 show that the proposed framework not only alleviates the interference of underwater image degradation, but also makes the mAP@0.5 reach 79.8% and 79.4% and improves the mAP@0.5 by 3.8% and 1.1%, respectively, when compared with the original YOLOv8 on URPC2019 and URPC2020, demonstrating that the proposed framework presents superior performance for the high-precision detection of marine organisms.

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Section: Related Workmentioning

confidence: 99%

An Improved YOLOv5-Based Underwater Object-Detection Framework

Jian

Zhang

Zhou

et al. 2023

Sensors

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“…On the basis of the Ghost Bottleneck when the stride is 1, a DWConv with a step size of 2 is inserted between the two Ghost modules for downsampling. The ghost module is a lightweight convolution module [37,38]. To reduce the model size and FLOPs, the CBS module in the neck of the YOLOv5s model was replaced with the Ghost module, and the original C3 module was replaced with the C3Ghost module.…”

Section: Optimization Of the Neck Networkmentioning

confidence: 99%

LSR-YOLO: A High-Precision, Lightweight Model for Sheep Face Recognition on the Mobile End

Zhang

Xuan

Jing

et al. 2023

Animals

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The accurate identification of sheep is crucial for breeding, behavioral research, food quality tracking, and disease prevention on modern farms. As a result of the time-consuming, expensive, and unreliable problems of traditional sheep-identification methods, relevant studies have built sheep face recognition models to recognize sheep through facial images. However, the existing sheep face recognition models face problems such as high computational costs, large model sizes, and weak practicality. In response to the above issues, this study proposes a lightweight sheep face recognition model named LSR-YOLO. Specifically, the ShuffleNetv2 module and Ghost module were used to replace the feature extraction module in the backbone and neck of YOLOv5s to reduce floating-point operations per second (FLOPs) and parameters. In addition, the coordinated attention (CA) module was introduced into the backbone to suppress non-critical information and improve the feature extraction ability of the recognition model. We collected facial images of 63 small-tailed Han sheep to construct a sheep face dataset and further evaluate the proposed method. Compared to YOLOv5s, the FLOPs and parameters of LSR-YOLO decreased by 25.5% and 33.4%, respectively. LSR-YOLO achieved the best performance on the sheep face dataset, and the mAP@0.5 reached 97.8% when the model size was only 9.5 MB. The experimental results show that LSR-YOLO has significant advantages in recognition accuracy and model size. Finally, we integrated LSR-YOLO into mobile devices and further developed a recognition system to achieve real-time recognition. The results show that LSR-YOLO is an effective method for identifying sheep. The method has high recognition accuracy and fast recognition speed, which gives it a high application value in mobile recognition and welfare breeding.

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“…Knowledge Tracing (KT) aims to gauge students' comprehension levels based on their historical interactions. The effectiveness of deep learning techniques in fields like speech processing (e.g., [20][21][22][23]) and computer vision (e.g., [24][25][26][27]) has inspired the development of deep learning-based KT models. One pioneering model is the Deep Knowledge Tracing (DKT) [16], which employs neural networks to capture intricate educational processes.…”

Section: Knowledge Tracingmentioning

confidence: 99%

Knowledge relation rank enhanced heterogeneous learning interaction modeling for neural graph forgetting knowledge tracing

Li,

Wang

2023

PLoS ONE

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Knowledge tracing models have gained prominence in educational data mining, with applications like the Self-Attention Knowledge Tracing model, which captures the exercise-knowledge relationship. However, conventional knowledge tracing models focus solely on static question-knowledge and knowledge-knowledge relationships, treating them with equal significance. This simplistic approach often succumbs to subjective labeling bias and lacks the depth to capture nuanced exercise-knowledge connections. In this study, we propose a novel knowledge tracing model called Knowledge Relation Rank Enhanced Heterogeneous Learning Interaction Modeling for Neural Graph Forgetting Knowledge Tracing. Our model mitigates the impact of subjective labeling by fine-tuning the skill relation matrix and Q-matrix. Additionally, we employ Graph Convolutional Networks (GCNs) to capture intricate interactions between students, exercises, and skills. Specifically, the Knowledge Relation Importance Rank Calibration method is employed to generate the skill relation matrix and Q-matrix. These calibrated matrices, alongside heterogeneous interactions, serve as input for the GCN to compute exercise and skill embeddings. Subsequently, exercise embeddings, skill embeddings, item difficulty, and contingency tables collectively contribute to an exercise relation matrix, which is then fed into an attention mechanism for predictions. Experimental evaluations on two publicly available educational datasets demonstrate the superiority of our proposed model over baseline models, evidenced by enhanced performance across three evaluation metrics.

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GBH-YOLOv5: Ghost Convolution with BottleneckCSP and Tiny Target Prediction Head Incorporating YOLOv5 for PV Panel Defect Detection

Cited by 59 publications

References 53 publications

An Improved YOLOv5-Based Underwater Object-Detection Framework

An Improved YOLOv5-Based Underwater Object-Detection Framework

LSR-YOLO: A High-Precision, Lightweight Model for Sheep Face Recognition on the Mobile End

Knowledge relation rank enhanced heterogeneous learning interaction modeling for neural graph forgetting knowledge tracing

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