Cascade Object Detection and Remote Sensing Object Detection Method Based on Trainable Activation Function

Abstract

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.

Confidence Score: The Forgotten Dimension of Object Detection Performance Evaluation

“…The COCO dataset is one of the most used and evaluated datasets for object classification DNN architecture and hyperparameter tuning benchmarks. Previous research indicates that the COCO dataset is well-suited for model robustness and efficiency analysis [ 32 ]. Since the COCO dataset could be considered to be the reference dataset for object detection and provides annotations for instance segmentation as well, we consider it necessary to use it as a basis.…”

Divide to Attend: A Multiple Receptive Field Attention Module for Object Detection in Remote Sensing Images

“…Markov random field-fully convolutional network (M-FCN) [33] mainly improves the detection performance of aircraft through the regional proposal generation stage based on multi Markov random field. AAF-Faster RCNN [34] applies the Additive Activation Function (AAF) to the Faster Region-based CNN (RCNN) object detection with higher efficiency and more robust performance in remote sensing images. The Split-Merge-Enhancement network (SME-Net) [35] detects objects with significant scale differences in remote sensing images by Offset-Error Rectification (OER), Feature Split-and-Merge (FSM), and Object Saliency Enhancement (OSE).…”

Context Information Refinement for Few-Shot Object Detection in Remote Sensing Images

“…Overall Architecture Depending on the high accuracy and recall, a two-stage detector is widely employed for object detection in the remote sensing of images, such as various improved algorithms based on the popular Faster R-CNN [10]. Shivappriya et al [42] applied the Additive Activation Function (AAF) to Faster R-CNN to improve the efficiency of object detection. In the mainstream detection frameworks (i.e., Detectron2 [43] and MMDetection [44]), two-stage object detector generally includes multiple modules, such as Backbone, FPN [26], RPN and Roi Feature Extractor.…”