Mini-YOLOv3: Real-Time Object Detector for Embedded Applications

Mao, Qi-Chao; Sun, Hong-Mei; Liu, Yanbo; Jia, Rui-Sheng

doi:10.1109/access.2019.2941547

Cited by 164 publications

(87 citation statements)

References 35 publications

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“…Detecting real-time objects are very challenging due to the limited memory and computation power. To address these challenges, QI-CHAO et al [120] suggested a lightweight network based on Darknet-53, with a Multi-scale feature pyramid for multi-scale detection object called Mini-YOLOv3.…”

Section: ) Yolo: You Only Look Oncementioning

confidence: 99%

Exploring Deep Learning-Based Architecture, Strategies, Applications and Current Trends in Generic Object Detection: A Comprehensive Review

Aziz

Salam²,

Sheikh³

et al. 2020

IEEE Access

100

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Object detection is a fundamental but challenging issue in the field of generic image analysis; it plays an important role in a wide range of applications and has been receiving special attention in recent years. Although there are enomerous methods exist, an in-depth review of the literature concerning generic detection remains. This paper provides a comprehensive survey of recent advances in visual object detection with deep learning. Covering about 300 publications that we survey 1) region proposal-based object detection methods such as R

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Section: ) Yolo: You Only Look Oncementioning

confidence: 99%

Exploring Deep Learning-Based Architecture, Strategies, Applications and Current Trends in Generic Object Detection: A Comprehensive Review

Aziz

Salam²,

Sheikh³

et al. 2020

IEEE Access

100

View full text Add to dashboard Cite

show abstract

“…Loss function of YOLO v3 in this paper is composed of mean variance and error [ 37 ]. Specifically, it is mainly divided into three parts for the calculation of offset losses, midpoint coordinate of parabola in GPR image prediction error gprErr, V-IoU prediction error viouErr, and classification error clsErr [ 38 ].…”

Section: Methodsmentioning

confidence: 99%

Real-Time Pattern-Recognition of GPR Images with YOLO v3 Implemented by Tensorflow

Zhen

Luo

et al. 2020

Sensors

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Artificial intelligence (AI) is widely used in pattern recognition and positioning. In most of the geological exploration applications, it needs to locate and identify underground objects according to electromagnetic wave characteristics from the ground-penetrating radar (GPR) images. Currently, a few robust AI approach can detect targets by real-time with high precision or automation for GPR images recognition. This paper proposes an approach that can be used to identify parabolic targets with different sizes and underground soil or concrete structure voids based on you only look once (YOLO) v3. With the TensorFlow 1.13.0 developed by Google, we construct YOLO v3 neural network to realize real-time pattern recognition of GPR images. We propose the specific coding method for the GPR image samples in Yolo V3 to improve the prediction accuracy of bounding boxes. At the same time, K-means algorithm is also applied to select anchor boxes to improve the accuracy of positioning hyperbolic vertex. For some instances electromagnetic-vacillated signals may occur, which refers to multiple parabolic electromagnetic waves formed by strong conductive objects among soils or overlapping waveforms. This paper deals with the vacillating signal similarity intersection over union (IoU) (V-IoU) methods. Experimental result shows that the V-IoU combined with non-maximum suppression (NMS) can accurately frame targets in GPR image and reduce the misidentified boxes as well. Compared with the single shot multi-box detector (SSD), YOLO v2, and Faster-RCNN, the V-IoU YOLO v3 shows its superior performance even when implemented by CPU. It can meet the real-time output requirements by an average 12 fps detected speed. In summary, this paper proposes a simple and high-precision real-time pattern recognition method for GPR imagery, and promoted the application of artificial intelligence or deep learning in the field of the geophysical science.

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“…Although segmentation has the ability to separate different objects, it cannot distinguish the objects of interest from others. Typically, semantic information is used to recognize the objects of interest [28].…”

Section: A Image Semantic Object Detectionmentioning

confidence: 99%

“…where f ix is the function of taking an integer in the direction of minus infinity [28]. Thus the computed parameter r can be used to adjusts the compensation region adaptively by the size of the object.…”

Section: ) Calculation Of Center Depthmentioning

confidence: 99%

Image Object Extraction Based on Semantic Detection and Improved K-Means Algorithm

et al. 2020

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Object extraction is an important tool in many applications within the image processing and computer vision communities. You Only Look Once version 3 (YOLOv3) has been extensively applied to many fields as a state-of-the-art technique for object semantic detection. Despite its numerous characteristics, YOLOv3 has to be combined with appropriate image segmentation technologies to achieve effective 2D object extraction in real-time monitoring, robot navigation, and target search. In this paper, the K-means algorithm is applied to the segmentation of depth images. Considering the inherent sensitivity to the randomness of the initial cluster center and the uncertainty of cluster number K in the initialization phase of the K-means algorithm, this paper proposes a new method that combines the semantic image information with the image depth information. Specifically, this method proposed to pre-classify the center depth of the object to determine the appropriate value of K required in the K-means algorithm. At the same time, the proposed algorithm improves the selection of the initial center via the maximin method. This paper introduces a multi-parameter extraction method to enable to correctly identify the object of interest after image segmentation. The technique considers three parameters to achieve this: i) the elements of size, ii) the connected domain, and iii) the diagonal detection. Experiments using open-source datasets demonstrate that the average processing time and the segmentation accuracy of the improved K-means algorithm are 20.36% faster and 3.12% higher than the conventional K-means algorithm, respectively. The extraction accuracy of the proposed method is 6.69% higher than that of the SuperCut extraction method.

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Mini-YOLOv3: Real-Time Object Detector for Embedded Applications

Cited by 164 publications

References 35 publications

Exploring Deep Learning-Based Architecture, Strategies, Applications and Current Trends in Generic Object Detection: A Comprehensive Review

Exploring Deep Learning-Based Architecture, Strategies, Applications and Current Trends in Generic Object Detection: A Comprehensive Review

Real-Time Pattern-Recognition of GPR Images with YOLO v3 Implemented by Tensorflow

Image Object Extraction Based on Semantic Detection and Improved K-Means Algorithm

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