Small Object Detection in Traffic Scenes Based on YOLO-MXANet

He, Xiaowei; Cheng, Rao; Zheng, Zhonglong; Wang, Zeji

doi:10.3390/s21217422

Cited by 27 publications

(13 citation statements)

References 34 publications

(37 reference statements)

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“…Overfitting may occur during the training of a deep CNN. An effective way to avoid overfitting is to increase the amount of training data [ 43 , 44 , 45 ]. However, the acquisition of data requires considerable human and financial resources, and in some cases, images cannot be obtained.…”

Section: Methodsmentioning

confidence: 99%

Experimental Study of Garlic Root Cutting Based on Deep Learning Application in Food Primary Processing

Yang

Zhang

et al. 2022

Foods

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Garlic root cutting is generally performed manually; it is easy for the workers to sustain hand injuries, and the labor efficiency is low. However, the significant differences between individual garlic bulbs limit the development of an automatic root cutting system. To address this problem, a deep learning model based on transfer learning and a low-cost computer vision module was used to automatically detect garlic bulb position, adjust the root cutter, and cut garlic roots on a garlic root cutting test bed. The proposed object detection model achieved good performance and high detection accuracy, running speed, and detection reliability. The visual image of the output layer channel of the backbone network showed the high-level features extracted by the network vividly, and the differences in learning of different networks clearly. The position differences of the cutting lines predicted by different backbone networks were analyzed through data visualization. The excellent and stable performance indicated that the proposed model had learned the correct features in the data of different brightness. Finally, the root cutting system was verified experimentally. The results of three experiments with 100 garlic bulbs each indicated that the mean qualified value of the system was 96%. Therefore, the proposed deep learning system can be applied in garlic root cutting which belongs to food primary processing.

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Section: Methodsmentioning

confidence: 99%

Experimental Study of Garlic Root Cutting Based on Deep Learning Application in Food Primary Processing

Yang

Zhang

et al. 2022

Foods

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“…IIHNet is a convolution-based network based on three key concepts: (i) information fusion; (ii) information exchange between different resolutions and modules; and (iii) a multiscale network. Furthermore, [123] proposed a lightweight network known as YOLO-MXANet which uses a powerful backbone based on the MobileNext [124] named SA-MobileNeXt, as a mean to incorporate both spatial and channel attention. Along with the addition of another scale from the shallower layers to improve the performance of SOD, the number of parameters was markedly reduced from 61.5 M to 13.8 M. The authors in [125] proposed a single stage SODNet composed of an adaptively spatial parallel convolution module (ASPConv) and a fast multi-scale fusion module (FMF) to optimize the spatial information extraction and to fuse the spatial and semantic information.…”

Section: Feature Learningmentioning

confidence: 99%

A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance

Rekavandi¹,

Liu²,

Boussaid³

et al. 2022

Preprint

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Small object detection (SOD) in optical images and videos is a challenging problem that even state-of-the-art generic object detection methods fail to accurately localize and identify such objects. Typically, small objects appear in real-world due to large camera-object distance. Because small objects occupy only a small area in the input image (e.g., less than 10%), the information extracted from such a small area is not always rich enough to support decision making. Multidisciplinary strategies are being developed by researchers working at the interface of deep learning and computer vision to enhance the performance of SOD deep learning based methods. In this paper, we provide a comprehensive review of over 160 research papers published between 2017 and 2022 in order to survey this growing subject. This paper summarizes the existing literature and provide a taxonomy that illustrates the broad picture of current research. We investigate how to improve the performance of small object detection in maritime environments, where increasing performance is critical. By establishing a connection between generic and maritime SOD research, future directions have been identified. In addition, the popular datasets that have been used for SOD for generic and maritime applications are discussed, and also well-known evaluation metrics for the state-of-the-art methods on some of the datasets are provided.

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“…These works achieved high detection accuracy, but the detection speeds when used on traffic scenes demonstrated the limitations of these methods. He et al [15] used a one-stage detector called YOLO-MXANet to perform small object detection in traffic scenes to improve the detection speed. Based on mask R-CNN, the mask scoring (MS) R-CNN approach [16] uses a mask IoU head to learn the predicted mask quality and then obtain a new network structure; this approach combines the characteristics of the example with the corresponding predictive mask to enable regression to the mask IoU.…”

Section: A Object Detectionmentioning

confidence: 99%

ALODAD: An Anchor-Free Lightweight Object Detector for Autonomous Driving

et al. 2022

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Vision-based object detection is an essential element of autonomous driving. Because vehicles will typically have limited on-board computing resources, a small-sized detection model is required. At the same time, high object detection accuracy and real-time inference detection speeds are required to ensure safety while driving. In this paper, an anchor-free lightweight object detector for autonomous driving called ALODAD is proposed. ALODAD incorporates an attention scheme into the lightweight neural network GhostNet, and builds an anchor-free detection framework to achieve lower computation costs and provide parameters with high detection accuracy. Specifically, the lightweight backbone neural network integrates a convolutional block attention model that analyzes the valuable features from traffic scene images to generate an accurate bounding box, and then constructs feature pyramids for multi-scale object detection. The proposed method adds an intersection over union (IoU) branch into the decoupled detector to rank the vast numbers of candidate detections accurately. To increase the data diversity, data augmentation is used during training. Extensive experiments based on benchmarks demonstrate that the proposed method offers an improved performance when compared with the baseline. The proposed method can achieve increased detection accuracy while also meeting the real-time requirements of autonomous driving. The proposed method is compared with the YOLOv5 and RetinaNet models and obtains 98.7% and 94.5% for the average precision metrics AP50 and AP75, respectively, on the BCTSDB dataset.

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Small Object Detection in Traffic Scenes Based on YOLO-MXANet

Cited by 27 publications

References 34 publications

Experimental Study of Garlic Root Cutting Based on Deep Learning Application in Food Primary Processing

Experimental Study of Garlic Root Cutting Based on Deep Learning Application in Food Primary Processing

A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance

ALODAD: An Anchor-Free Lightweight Object Detector for Autonomous Driving

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