An Improved Mobilenetv3-Yolov5 Infrared Target Detection Algorithm Based on Attention Distillation

Jin, Ronglu; Xu, Yidong; Xue, Wei; Li, Beiming; Yang, Yingwei; Chen, Wenjian

doi:10.1007/978-3-030-94551-0_22

Cited by 9 publications

(4 citation statements)

References 22 publications

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“…In YOLOv5, the neck layer aids in transforming the features extracted from the backbone network into a format suitable for object detection. The neck also consists of the FPN (Feature Pyramid Network) + PAN (Path Aggregation Network) [17] structure, but with the addition of the CSP2 (Cross Stage Paritial Network2) structure to enhance the network's feature fusion ability. In this section, the SPP (Spatial Pyramid Pooling) structure is replaced with the SPPF (Spatial Pyramid Pooling-Fast) structure, which involves passing the input through multiple 5 × 5 max pooling layers in series, as shown in Figure 2.…”

Section: Detection Principle Of Yolov5mentioning

confidence: 99%

Surface Defect Detection for Automated Tape Laying and Winding Based on Improved YOLOv5

Wen

Jia-jun³

2023

Materials

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To address the issues of low detection accuracy, slow detection speed, high missed detection rate, and high false detection rate in the detection of surface defects on pre-impregnated composite materials during the automated tape laying and winding process, an improved YOLOv5 (You Only Look Once version 5) algorithm model was proposed to achieve the high-precision, real-time detection of surface defects. By leveraging this improvement, the necessity for frequent manual interventions, inspection interventions, and subsequent rework during the automated lay-up process of composite materials can be significantly reduced. Firstly, to improve the detection accuracy, an attention mechanism called “CA (coordinate attention)” was introduced to enhance the feature extraction ability, and a Separate CA structure was used to improve the detection speed. Secondly, we used an improved loss function “SIoU (SCYLLA-Intersection over Union) loss” to replace the original “CIoU (Complete-Intersection over Union) loss”, which introduced an angle loss as a penalty term to consider the directional factor and improve the stability of the target box regression. Finally, Soft-SIoU-NMS was used to replace the original NMS (non-maximum suppression) of YOLOv5 to improve the detection of overlapping defects. The results showed that the improved model had a good detection performance for surface defects on pre-impregnated composite materials during the automated tape laying and winding process. The FPS (frames per second) increased from 66.7 to 72.1, and the mAP (mean average precision) of the test set increased from 92.6% to 97.2%. These improvements ensured that the detection accuracy, as measured by the mAP, surpassed 95%, while maintaining a detection speed of over 70 FPS, thereby meeting the requirements for real-time online detection.

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Section: Detection Principle Of Yolov5mentioning

confidence: 99%

Surface Defect Detection for Automated Tape Laying and Winding Based on Improved YOLOv5

Wen

Jia-jun³

2023

Materials

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“…The other models expand upon and enhance the YOLOv5s model by increasing the network depth and width, resulting in improved accuracy. However, this increased complexity also leads to higher hardware requirements for computing devices [26]. Compared to two-stage deep learning models like Fast R-CNN, the YOLO series of models do not require target extraction based on candidate frames for recognition results.…”

Section: Introductionmentioning

confidence: 99%

Wildlife Real-Time Detection in Complex Forest Scenes Based on YOLOv5s Deep Learning Network

Ma,

Dong,

Xia

et al. 2024

Remote Sensing

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With the progressively deteriorating global ecological environment and the gradual escalation of human activities, the survival of wildlife has been severely impacted. Hence, a rapid, precise, and reliable method for detecting wildlife holds immense significance in safeguarding their existence and monitoring their status. However, due to the rare and concealed nature of wildlife activities, the existing wildlife detection methods face limitations in efficiently extracting features during real-time monitoring in complex forest environments. These models exhibit drawbacks such as slow speed and low accuracy. Therefore, we propose a novel real-time monitoring model called WL-YOLO, which is designed for lightweight wildlife detection in complex forest environments. This model is built upon the deep learning model YOLOv5s. In WL-YOLO, we introduce a novel and lightweight feature extraction module. This module is comprised of a deeply separable convolutional neural network integrated with compression and excitation modules in the backbone network. This design is aimed at reducing the number of model parameters and computational requirements, while simultaneously enhancing the feature representation of the network. Additionally, we introduced a CBAM attention mechanism to enhance the extraction of local key features, resulting in improved performance of WL-YOLO in the natural environment where wildlife has high concealment and complexity. This model achieved a mean accuracy (mAP) value of 97.25%, an F1-score value of 95.65%, and an accuracy value of 95.14%. These results demonstrated that this model outperforms the current mainstream deep learning models. Additionally, compared to the YOLOv5m base model, WL-YOLO reduces the number of parameters by 44.73% and shortens the detection time by 58%. This study offers technical support for detecting and protecting wildlife in intricate environments by introducing a highly efficient and advanced wildlife detection model.

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“…Attention mechanisms, such as Shuffle Attention (SA), Convolutional Block Attention Module (CBAM), and Coordinate Attention (CA), have been developed to achieve lightweight enhancements and can be easily integrated into mobile network modules [6]. In recent years, researchers have been actively exploring lightweight modules such as GhostNet, MobileNetV3, and BlazeFace [7,8]. Additionally, many scholars have been attempting to refine the backbone section of YOLOv5 with lightweight modules and incorporate attention mechanisms, aiming to strike a balance between accuracy and computational efficiency.…”

Section: Introductionmentioning

confidence: 99%

Improved Lightweight Multi-Target Recognition Model for Live Streaming Scenes

Li,

Qiao,

Chen

et al. 2023

Applied Sciences

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Nowadays, the commercial potential of live e-commerce is being continuously explored, and machine vision algorithms are gradually attracting the attention of marketers and researchers. During live streaming, the visuals can be effectively captured by algorithms, thereby providing additional data support. This paper aims to consider the diversity of live streaming devices and proposes an extremely lightweight and high-precision model to meet different requirements in live streaming scenarios. Building upon yolov5s, we incorporate the MobileNetV3 module and the CA attention mechanism to optimize the model. Furthermore, we construct a multi-object dataset specific to live streaming scenarios, including anchor facial expressions and commodities. A series of experiments have demonstrated that our model realized a 0.4% improvement in accuracy compared to the original model, while reducing its weight to 10.52%.

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An Improved Mobilenetv3-Yolov5 Infrared Target Detection Algorithm Based on Attention Distillation

Cited by 9 publications

References 22 publications

Surface Defect Detection for Automated Tape Laying and Winding Based on Improved YOLOv5

Surface Defect Detection for Automated Tape Laying and Winding Based on Improved YOLOv5

Wildlife Real-Time Detection in Complex Forest Scenes Based on YOLOv5s Deep Learning Network

Improved Lightweight Multi-Target Recognition Model for Live Streaming Scenes

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