DMS-YOLOv5: A Decoupled Multi-Scale YOLOv5 Method for Small Object Detection

Gao, Tianyu; Wushouer, Mairidan; Tuerhong, Gulanbaier

doi:10.3390/app13106124

Cited by 7 publications

(2 citation statements)

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“…To effectively detect small traffic sign targets, two primary challenges must be overcome [ 39 , 40 ]. Firstly, small objects have fewer pixels and weaker feature representation, resulting in limited representative feature information.…”

Section: Methodsmentioning

confidence: 99%

A Small Object Detection Algorithm for Traffic Signs Based on Improved YOLOv7

Wang

2023

Sensors

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Traffic sign detection is a crucial task in computer vision, finding wide-ranging applications in intelligent transportation systems, autonomous driving, and traffic safety. However, due to the complexity and variability of traffic environments and the small size of traffic signs, detecting small traffic signs in real-world scenes remains a challenging problem. In order to improve the recognition of road traffic signs, this paper proposes a small object detection algorithm for traffic signs based on the improved YOLOv7. First, the small target detection layer in the neck region was added to augment the detection capability for small traffic sign targets. Simultaneously, the integration of self-attention and convolutional mix modules (ACmix) was applied to the newly added small target detection layer, enabling the capture of additional feature information through the convolutional and self-attention channels within ACmix. Furthermore, the feature extraction capability of the convolution modules was enhanced by replacing the regular convolution modules in the neck layer with omni-dimensional dynamic convolution (ODConv). To further enhance the accuracy of small target detection, the normalized Gaussian Wasserstein distance (NWD) metric was introduced to mitigate the sensitivity to minor positional deviations of small objects. The experimental results on the challenging public dataset TT100K demonstrate that the SANO-YOLOv7 algorithm achieved an 88.7% mAP@0.5, outperforming the baseline model YOLOv7 by 5.3%.

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Section: Methodsmentioning

confidence: 99%

A Small Object Detection Algorithm for Traffic Signs Based on Improved YOLOv7

Wang

2023

Sensors

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“…Li et al [19] introduced the SE-Inception structure into the CNN framework, which enhances useful features and compresses redundant features, making the CNN model more versatile and robust. Gao et al [20] incorporated the CA [21] attention mechanism into the base network, reducing the number of parameters and making the features more clear.…”

Section: Introductionmentioning

confidence: 99%

SE-VisionTransformer: Hybrid Network for Diagnosing Sugarcane Leaf Diseases Based on Attention Mechanism

Sun,

Zhou,

Zhang

et al. 2023

Sensors

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Sugarcane is an important raw material for sugar and chemical production. However, in recent years, various sugarcane diseases have emerged, severely impacting the national economy. To address the issue of identifying diseases in sugarcane leaf sections, this paper proposes the SE-VIT hybrid network. Unlike traditional methods that directly use models for classification, this paper compares threshold, K-means, and support vector machine (SVM) algorithms for extracting leaf lesions from images. Due to SVM’s ability to accurately segment these lesions, it is ultimately selected for the task. The paper introduces the SE attention module into ResNet-18 (CNN), enhancing the learning of inter-channel weights. After the pooling layer, multi-head self-attention (MHSA) is incorporated. Finally, with the inclusion of 2D relative positional encoding, the accuracy is improved by 5.1%, precision by 3.23%, and recall by 5.17%. The SE-VIT hybrid network model achieves an accuracy of 97.26% on the PlantVillage dataset. Additionally, when compared to four existing classical neural network models, SE-VIT demonstrates significantly higher accuracy and precision, reaching 89.57% accuracy. Therefore, the method proposed in this paper can provide technical support for intelligent management of sugarcane plantations and offer insights for addressing plant diseases with limited datasets.

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