Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles

Li, Hu‐Lin; Li, Jun; Wei, Hanbing; Liu, Zheng; Zhan, Zhenfei; Ren, Qiushi

doi:10.48550/arxiv.2206.02424

Cited by 130 publications

(154 citation statements)

References 0 publications

Supporting

Mentioning

154

Contrasting

Order By: Relevance

“…The computational cost of GSConv2D is only 60%-70% of standard convolution, but the contribution to the model learning ability is comparable to standard convolution [29]. Fig.…”

Section: B Overview Of the Yolo-gs Frameworkmentioning

confidence: 92%

An Ultralightweight Object Detection Network for Empty-Dish Recycling Robots

Yue

Meng

2023

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

The emergence of empty-dish recycling robots has alleviated problems, such as labor shortages, caused by an aging population. The detection and grasping of dishes play a crucial role in empty-dish recycling robots. However, due to the limited resources of edge devices, traditional object detection models require more space to store parameters and much computational overhead, limiting the development of empty-dish recycling robots. Therefore, this article proposes an ultralightweight dish detection model YOLO-GS for an empty-dish recycling robot. We use the modified CSPDarknet as the backbone structure and design an ultralightweight neck structure for efficient feature fusion. Meanwhile, we design a lightweight head structure for object classification and bounding box coordinate regression by combining ghost shuffle convolution (GSConv2D) and the anchor-free method. For the empty-dish recycling robot to grasp the dishes, we design a dish grasp point extraction algorithm using image processing. Finally, TensorRT is used to optimize and accelerate the model for efficient and intelligent detection of dishes on the NVIDIA Jetson Xavier NX. The experimental results show that YOLO-GS achieves 99.380% mean average precision (mAP) with a parameter amount of 0.606 M. The inference speed of the TensorRT-optimized YOLO-GS algorithm reaches 31.371 FPS, which meets the needs of real-time dish detection by the empty-dish recycling robot. The image of the empty-dish recycling robot demo is available at https://www.youtube.com/watch?v=pCBo1nzm3qU&t=22s.

show abstract

“…The computational cost of GSConv2D is only 60%-70% of standard convolution, but the contribution to the model learning ability is comparable to standard convolution [29]. Fig.…”

Section: B Overview Of the Yolo-gs Frameworkmentioning

confidence: 92%

An Ultralightweight Object Detection Network for Empty-Dish Recycling Robots

Yue

Meng

2023

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

show abstract

“…GSConv is a new lightweight convolution method proposed in 2022 [ 25 ]. The structure, shown in Figure 4 , enhances the non-linear representation by adding DSC layers and a shuffle, which preserves the hidden connections between each channel as much as possible with less time complexity.…”

Section: Methodsmentioning

confidence: 99%

Maize-YOLO: A New High-Precision and Real-Time Method for Maize Pest Detection

Yang

Xing

Wang

et al. 2023

Insects

View full text Add to dashboard Cite

The frequent occurrence of crop pests and diseases is one of the important factors leading to the reduction of crop quality and yield. Since pests are characterized by high similarity and fast movement, this poses a challenge for artificial intelligence techniques to identify pests in a timely and accurate manner. Therefore, we propose a new high-precision and real-time method for maize pest detection, Maize-YOLO. The network is based on YOLOv7 with the insertion of the CSPResNeXt-50 module and VoVGSCSP module. It can improve network detection accuracy and detection speed while reducing the computational effort of the model. We evaluated the performance of Maize-YOLO in a typical large-scale pest dataset IP102. We trained and tested against those pest species that are more damaging to maize, including 4533 images and 13 classes. The experimental results show that our method outperforms the current state-of-the-art YOLO family of object detection algorithms and achieves suitable performance at 76.3% mAP and 77.3% recall. The method can provide accurate and real-time pest detection and identification for maize crops, enabling highly accurate end-to-end pest detection.

show abstract

“…The latest version of YOLOv5 improves DIOU by replacing it with the CIOU loss function. CIOU adds the loss of the detection frame scale to DIOU by increasing the loss of the length and width so that the prediction frame will be more in line with the real frame [ 33 ]. The CIOU calculation equations are given in Equations (3)–(6):

where the parameters

and

denote the area of Ground truth bounding box and the area of predicted bounding box;

denotes the Euclidean distance of the diagonal vertices of the closed box;

denotes the Euclidean distance of the center of mass of Ground truth bounding box and predicted bounding box;

denotes the distance of the center points of the two boxes;

is the balance parameters, and

is the indicators to evaluate whether the is an index to evaluate the consistency of the aspect ratio between the Ground truth bounding box and the predicted bounding box.…”

Section: Methodsmentioning

confidence: 99%

Adverse Weather Target Detection Algorithm Based on Adaptive Color Levels and Improved YOLOv5

Yao

Fan

et al. 2022

Sensors

View full text Add to dashboard Cite

With the continuous development of artificial intelligence and computer vision technology, autonomous vehicles have developed rapidly. Although self-driving vehicles have achieved good results in normal environments, driving in adverse weather can still pose a challenge to driving safety. To improve the detection ability of self-driving vehicles in harsh environments, we first construct a new color levels offset compensation model to perform adaptive color levels correction on images, which can effectively improve the clarity of targets in adverse weather and facilitate the detection and recognition of targets. Then, we compare several common one-stage target detection algorithms and improve on the best-performing YOLOv5 algorithm. We optimize the parameters of the Backbone of the YOLOv5 algorithm by increasing the number of model parameters and incorporating the Transformer and CBAM into the YOLOv5 algorithm. At the same time, we use the loss function of EIOU to replace the loss function of the original CIOU. Finally, through the ablation experiment comparison, the improved algorithm improves the detection rate of the targets, with the mAP reaching 94.7% and the FPS being 199.86.

show abstract

Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles

Cited by 130 publications

References 0 publications

An Ultralightweight Object Detection Network for Empty-Dish Recycling Robots

An Ultralightweight Object Detection Network for Empty-Dish Recycling Robots

Maize-YOLO: A New High-Precision and Real-Time Method for Maize Pest Detection

Adverse Weather Target Detection Algorithm Based on Adaptive Color Levels and Improved YOLOv5

Contact Info

Product

Resources

About