Cascade Object Detection and Remote Sensing Object Detection Method Based on Trainable Activation Function
| Autor: | M. Jasmine Pemeena Priyadarsini, Andrzej Stateczny, B. D. Parameshachari, C. Puttamadappa, S. N. Shivappriya |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Additive Activation Function
Computer science Science Activation function 0211 other engineering and technologies Faster Region based Convolution Neural Network cascade object detection Fourier series and linear combination of activation function remote sensing 02 engineering and technology Overfitting Convolutional neural network 0202 electrical engineering electronic engineering information engineering 021101 geological & geomatics engineering Remote sensing computer.programming_language Pascal (programming language) Object (computer science) Object detection Data set Benchmark (computing) General Earth and Planetary Sciences 020201 artificial intelligence & image processing computer |
| Zdroj: | Remote Sensing; Volume 13; Issue 2; Pages: 200 Remote Sensing, Vol 13, Iss 200, p 200 (2021) |
| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs13020200 |
| Popis: | Object detection is an important process in surveillance system to locate objects and it is considered as major application in computer vision. The Convolution Neural Network (CNN) based models have been developed by many researchers for object detection to achieve higher performance. However, existing models have some limitations such as overfitting problem and lower efficiency in small object detection. Object detection in remote sensing hasthe limitations of low efficiency in detecting small object and the existing methods have poor localization. Cascade Object Detection methods have been applied to increase the learning process of the detection model. In this research, the Additive Activation Function (AAF) is applied in a Faster Region based CNN (RCNN) for object detection. The proposed AAF-Faster RCNN method has the advantage of better convergence and clear bounding variance. The Fourier Series and Linear Combination of activation function are used to update the loss function. The Microsoft (MS) COCO datasets and Pascal VOC 2007/2012 are used to evaluate the performance of the AAF-Faster RCNN model. The proposed AAF-Faster RCNN is also analyzed for small object detection in the benchmark dataset. The analysis shows that the proposed AAF-Faster RCNN model has higher efficiency than state-of-art Pay Attention to Them (PAT) model in object detection. To evaluate the performance of AAF-Faster RCNN method of object detection in remote sensing, the NWPU VHR-10 remote sensing data set is used to test the proposed method. The AAF-Faster RCNN model has mean Average Precision (mAP) of 83.1% and existing PAT-SSD512 method has the 81.7%mAP in Pascal VOC 2007 dataset. |
| Databáze: | OpenAIRE |
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