A novel target detection and localization method in indoor environment for mobile robot based on improved YOLOv5

Qian, Weijie; Hu, Chunhua; Wang, Hanzhao; Lü, Li; Shi, Zefeng

doi:10.1007/s11042-023-14569-w

Cited by 5 publications

(2 citation statements)

References 29 publications

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“…Previous studies (Qian et al., 2023; Zhang et al., 2022) have explored the substitution of the activation function in the original network model with the SMU activation function, resulting in improved performance without a rise in the model's parameter count. This clearly demonstrates that the activation function influences the model's performance to some extent, making it an effective approach for model enhancement.…”

Section: Discussionmentioning

confidence: 99%

Nondestructive testing of runny salted egg yolk based on improved ConvNeXt‐T

Chen,

Chang,

Chen

et al. 2024

Journal of Food Science

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Salted egg yolks from salted duck eggs are widely utilized in the domestic and international food industry as both raw materials and ingredients. When salted egg yolks are not fully cured and matured, they exist in a fluid state, with a mixture of solid and liquid internally. Due to this composition, they are susceptible to deterioration during storage and usage, necessitating their detection and classification. In this study, a dataset specifically for salted egg yolks was established, and the ConvNeXt‐T model, employed as the benchmark model, underwent two notable improvements. First, a lightweight location‐aware circular convolution (ParC) was introduced, utilizing a ParC‐block to replace a portion of the original ConvNeXt‐T block. This enhancement aimed to overcome the limitations of convolution in extracting global feature information while integrating the global sensing capability of vision transformer and the localization capability of convolution. Additionally, the activation function was modified through substitution. These improvements resulted in the final model. Experimental results indicate that the enhanced model exhibits faster convergence on the custom salted egg yolk dataset compared to the baseline model. Furthermore, a significant reduction of model parameters by a factor of 4 led to a 2.167 percentage point improvement in the accuracy of the test set. The ParC‐ConvNeXt‐SMU‐T model achieved an accuracy of 96.833% with 26.8 million parameters. Notably, the improved model demonstrates exceptional effectiveness in recognizing salted egg yolks.Practical ApplicationThis study can be widely applied in the process of salted egg yolk production and quality inspection, which can improve the actual sorting efficiency of salted egg yolks and reduce the labor cost at the same time. It can also be used for nondestructive testing of salted egg yolks by governmental enterprises and other regulatory authorities.

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Section: Discussionmentioning

confidence: 99%

Nondestructive testing of runny salted egg yolk based on improved ConvNeXt‐T

Chen,

Chang,

Chen

et al. 2024

Journal of Food Science

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“…The key to using intelligent transportation systems to efficiently manage traffic is accurate vehicle recognition and identification [1]. As a distinctive representation of a car, license plates have unique letters and numerals.…”

Section: Introductionmentioning

confidence: 99%

Enhancing License Plate Recognition in Foggy Conditions Based on YOLOV5 and MOSAIC Method

Wang

2024

Advances in Transdisciplinary Engineering

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The high precision recognition accuracy of the license plate for driving system is a challenging issue, especially in low-light conditions, such as foggy days. This paper introduces a foggy object detection network based on YOLOV5 to extract the relevant features of license plate images and train them for detection tasks, and then introduces the MOSAIC enhancement algorithm to recognize license plates in real time. The whole recognition process is mainly divided into two processes: license plate positioning and license plate character recognition, which is conducive to reducing the accumulation of recognition errors and improving the efficiency of recognition. According to the test results, the detection accuracy of the enhanced YOLOV5 is significantly improved, and its ability to identify and manage traffic vehicles is improved, with accurate and effective vehicle information.

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