Performance Enhancement of YOLOv3 by Adding Prediction Layers with Spatial Pyramid Pooling for Vehicle Detection

Kim, Kwang-Ju; Kim, Pyong-Kun; Chung, Yun-Su; Choi, Doo−Hyun

doi:10.1109/avss.2018.8639438

Cited by 48 publications

(22 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Second, NMS selects the predicted box with the largest score; then the IoU coefficients of other remain bounding boxes and the current box are calculated. If the IoU value is greater than the predefined threshold, NMS will delete this bounding box [ 35 ]. This is a complete iterative process in which NMS is applied to select the maximum score bounding box for one target.…”

Section: Methodsmentioning

confidence: 99%

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

Zhen

Luo

et al. 2020

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

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

Zhen

Luo

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…After that, more algorithms were proposed, such as YOLOv2, YOLOv3, SSD (Single Shot Multibox Detector) and etc. [12,17,18]. Kim J et al [11] proposed a method that using multiple sensors to estimate vehicle position during autonomous driving for detecting and tracking moving objects in 2019.…”

Section: Related Workmentioning

confidence: 99%

Multi-object Recognition Method Based on Improved YOLOv2 Model

Shi

Li²,

Tang³

et al. 2021

ITC

View full text Add to dashboard Cite

A method of vehicle multi-object identification and classification based on the YOLOv2 algorithm is proposed to solve the problems of low detection rate, poor robustness, and unsatisfactory classification effect for the classical multi-object detection and vehicle type classification on real road environment. Based on the YOLOv2 algorithm, the network structure of YOLOv2-voc is improved according to the actual road conditions. The classification training model was obtained based on the ImageNet data and fine-tuning technology, according to the analysis of training results and vehicle object characteristics. This paper proposed the improved vehicle identification classification network structure, namely called YOLOv2-voc_mul. In order to verify the validity of the detection method, experiments are performed using samples from simple backgrounds and complex backgrounds and compared with the existing YOLOv2, YOLOv2-voc, and YOLOv3 models after 70000 iterations, respectively. The results show that the proposed YOLOv2-voc_mul model has an accuracy of 98.6% under the simple background, and the mAP (mean Average Precision) of different models reaches 87.81%. Under the complex background, the improved YOLOv2-voc_mul model has an average accuracy of 92.09% and 89.64% for single and multi-object detection of four different models. In summary, our proposed method has better accuracy, a low false detection rate, and good robustness.

show abstract

“…The experimental results demonstrated that the proposed method could achieve high detection accuracy in various illumination conditions. Kim et al added two more prediction layers in YOLOv3 to effectively detect vehicles in different scales and inserted the spatial pyramid pooling networks between backbone network and FPN to improve accuracy by increasing feature quantity [22]. The proposed models have achieved the state-of-the-art average precision performance on the examined dataset.…”

Section: Introductionmentioning

confidence: 99%

Detection of Road Objects With Small Appearance in Images for Autonomous Driving in Various Traffic Situations Using a Deep Learning Based Approach

Xie

Yan

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Performance Enhancement of YOLOv3 by Adding Prediction Layers with Spatial Pyramid Pooling for Vehicle Detection

Cited by 48 publications

References 10 publications

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

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

Multi-object Recognition Method Based on Improved YOLOv2 Model

Detection of Road Objects With Small Appearance in Images for Autonomous Driving in Various Traffic Situations Using a Deep Learning Based Approach

Contact Info

Product

Resources

About