Lightweight object detection algorithm for robots with improved YOLOv5

Liu, Gang; Hu, Yanxin; Chen, Zhiyu; Guo, Jianwei; Ni, Peng

doi:10.1016/j.engappai.2023.106217

Cited by 65 publications

(8 citation statements)

References 33 publications

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“…The YOLOv8 model is a significant update in the YOLO series, released by Ultralytics in 2023. The model replaces the C3 module in the YOLOv5 [35] model with the C2f module, which is applied to the backbone and neck networks and is intended to enhance the fusion of features at different scales for more effective object detection [36]. The head network of the model transformed from a coupled head, where the tasks of object classification and bounding box regression are intertwined, to a decoupled head, which separates these tasks to improve the precision and efficiency of object detection.…”

Section: High-precision Rail Flaw Detection Methods Based On Yolov8mentioning

confidence: 99%

Improved YOLOv8 for B-scan image flaw detection of the heavy-haul railway

Yu,

Liu,

Cao

et al. 2024

Meas. Sci. Technol.

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With the high speed and heavy duty of railway transportation, internal flaw detection of railway rails has become a hot issue. Existing rail flaw detection systems have problems of low detection accuracy and occasional missed flaw detection. In this paper, a high-precision flaw detection based on data augmentation and YOLOv8 improvement is proposed. Firstly, three data augmentation algorithms based on the characteristics of B-scan images are designed to enrich the dataset of rail flaws. Then, the small target detection layer and the cross-layer connectivity module are added to capture more information for small targets. Finally, the introduction of dynamic weights to coordinate attention can adjust the attentional weights and capture long-range information. The experimental results show that the mAP50 of the model after data enhancement and algorithm improvement is 97.9%, which is improved by 4.4% from the baseline model, and the FPS is 64.52. The proposed method effectively detects many typical flaws, including the railhead flaw, rail jaw flaw, screw hole crack, and bottom flaw, which can provide technology supports for on-site maintenance staff.

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Section: High-precision Rail Flaw Detection Methods Based On Yolov8mentioning

confidence: 99%

Improved YOLOv8 for B-scan image flaw detection of the heavy-haul railway

Yu,

Liu,

Cao

et al. 2024

Meas. Sci. Technol.

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“…The data sets selected in this paper are: Common Objects in Context (COCO) dataset (Deng et al, 2023 ), Pascal VOC dataset (Liu et al, 2023 ), ISPRS test project Udacity AI for Robotics Dataset (Ribeiro et al, 2023 ), ImageNet Dataset (Liu et al, 2023 ).…”

Section: Methodsmentioning

confidence: 99%

Education robot object detection with a brain-inspired approach integrating Faster R-CNN, YOLOv3, and semi-supervised learning

Hong,

Dong,

Deng

et al. 2024

Front. Neurorobot.

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The development of education robots has brought tremendous potential and opportunities to the field of education. These intelligent machines can interact with students in classrooms and learning environments, providing personalized educational support. To enable education robots to fulfill their roles, they require accurate object detection capabilities to perceive and understand the surrounding environment of students, identify targets, and interact with them. Object detection in complex environments remains challenging, as classrooms or learning scenarios involve various objects, backgrounds, and lighting conditions. Improving the accuracy and efficiency of object detection is crucial for the development of education robots. This paper introduces the progress of an education robot's object detection based on a brain-inspired heuristic method, which integrates Faster R-CNN, YOLOv3, and semi-supervised learning. By combining the strengths of these three techniques, we can improve the accuracy and efficiency of object detection in education robot systems. In this work, we integrate two popular object detection algorithms: Faster R-CNN and YOLOv3. We conduct a series of experiments on the task of education robot object detection. The experimental results demonstrate that our proposed optimization algorithm significantly outperforms individual algorithms in terms of accuracy and real-time performance. Moreover, through semi-supervised learning, we achieve better performance with fewer labeled samples. This will provide education robots with more accurate perception capabilities, enabling better interaction with students and delivering personalized educational experiences. It will drive the development of the field of education robots, offering innovative and personalized solutions for education.

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“…YOLOv5 is the most accurate model in the YOLO series of networks in recent years [ 25 ]. Its praised features include its more systematic and less redundant code.…”

Section: Methodsmentioning

confidence: 99%

A Lightweight Recognition Method for Rice Growth Period Based on Improved YOLOv5s

Liu

Wang²,

Zhang

et al. 2023

Sensors

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The identification of the growth and development period of rice is of great significance to achieve high-yield and high-quality rice. However, the acquisition of rice growth period information mainly relies on manual observation, which has problems such as low efficiency and strong subjectivity. In order to solve these problems, a lightweight recognition method is proposed to automatically identify the growth period of rice: Small-YOLOv5, which is based on improved YOLOv5s. Firstly, the new backbone feature extraction network MobileNetV3 was used to replace the YOLOv5s backbone network to reduce the model size and the number of model parameters, thus improving the detection speed of the model. Secondly, in the feature fusion stage of YOLOv5s, we introduced a more lightweight convolution method, GsConv, to replace the standard convolution. The computational cost of GsConv is about 60–70% of the standard convolution, but its contribution to the model learning ability is no less than that of the standard convolution. Based on GsConv, we built a lightweight neck network to reduce the complexity of the network model while maintaining accuracy. To verify the performance of Small-YOLOv5s, we tested it on a self-built dataset of rice growth period. The results show that compared with YOLOv5s (5.0) on the self-built dataset, the number of the model parameter was reduced by 82.4%, GFLOPS decreased by 85.9%, and the volume reduced by 86.0%. The mAP (0.5) value of the improved model was 98.7%, only 0.8% lower than that of the original YOLOv5s model. Compared with the mainstream lightweight model YOLOV5s- MobileNetV3-Small, the number of the model parameter was decreased by 10.0%, the volume reduced by 9.6%, and the mAP (0.5:0.95) improved by 5.0%—reaching 94.7%—and the recall rate improved by 1.5%—reaching 98.9%. Based on experimental comparisons, the effectiveness and superiority of the model have been verified.

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Lightweight object detection algorithm for robots with improved YOLOv5

Cited by 65 publications

References 33 publications

Improved YOLOv8 for B-scan image flaw detection of the heavy-haul railway

Improved YOLOv8 for B-scan image flaw detection of the heavy-haul railway

Education robot object detection with a brain-inspired approach integrating Faster R-CNN, YOLOv3, and semi-supervised learning

A Lightweight Recognition Method for Rice Growth Period Based on Improved YOLOv5s

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