Objective

2

Object detection is a fundamental topic in computer vision. Deep learning-based object detection networks consist of two basic parts:feature extraction and object detection modules. Convolution neural networks (CNNs) are used to extract image features. On the one hand

deep feature maps are rich in object semantic information; sensitive to category information; lacking in detailed information; insensitive to position

translation

and rotation information; and widely used in classification tasks. On the other hand

shallow feature maps are rich in detailed information; sensitive to location

translation

and rotation information; lacking in semantic information; and insensitive to category information. The two main subtasks of object detection are classification and location. The former classifies the candidate regions and requires the semantic information of the object

whereas the latter locates the candidate regions and requires detailed information (e.g.

location). In the anchor mechanism of faster region-based CNN (R-CNN)

each anchor point of the predicted feature map corresponds to nine anchors with different sizes and ratios. A 1×1 convolution filter is used to predict the positions and confidence scores (i.e.

the probability that the object contained in the anchor box belongs to a certain category) of multiple anchors with different sizes. Therefore

for the anchors with different sizes that correspond to the anchor points

the same feature region on the feature map is used for prediction. This condition results in the mismatch between the feature region used in prediction and the corresponding anchor. To utilize the advantages of feature maps with different depths and overcome the mismatch problem in the anchor mechanism to accurately solve the problem of multi-scale object detection

we present a single-stage object detection model using convolution filter pyramid and atrous convolution.

Method

2

Feature information is fused in a variety of ways. First

multiple convolutional layers are added to the feature extraction network. The feature information in these layers is fused layer by layer (from the deep layers to the shallow ones) through pixel-by-pixel addition

thereby forming feature maps with rich semantic information and detailed information. Second

to further enhance the fusion of feature information

feature maps with different stages are concatenated for the fusion feature maps obtained in the previous step. To address the mismatch between the feature region and the corresponding anchor used for prediction

this study introduces a convolution filter pyramid structure into the anchor mechanism to detect objects with different sizes. Consequently

the sizes of the convolution filter corresponding to the anchors with different sizes is distinct and those corresponding to anchors with equal sizes but different ratios are the same. This condition alleviates the mismatch problem. In addition

the model uses the atrous convolution mechanism to design a convolution filter with different receptive fields because the large-scale convolution filter increases the number of parameters and the time complexity should be reduced. Under the action of convolution filters with different sizes

the prediction tensors (i.e.

feature maps) of different resolutions are generated on the feature maps with rich semantic and detailed information. The model determines the number of anchors according to the generated prediction tensors. The number of small anchors corresponding to small objects is large

whereas those corresponding to large objects is small

thereby reducing the number of anchors.

Result

2

The proposed method was tested and evaluated on PASCAL visual object classes (VOC) and UCAS-AOD remote sensing datasets

respectively. The code was implemented on the Caffe deep learning framework

where some components of the Caffe open-source library of single-shot multibox detector (SSD) and deconvolutional single-shot detector (DSSD) were utilized. All experiments were performed on an HP workstation with a Titan X GPU. SSD was used as the pre-training model of the proposed method. The model was fine-tuned on PASCAL VOC and UCAS-AOD and the performance was evaluated using the mean average precision (mAP) on VOC2007 and UCAS-AOD test sets. The proposed method was then compared with other advanced deep learning object detection methods in terms of mAP results and detection speed. Experimental results show that on the PASCAL VOC2007 test set

the proposed model can achieve 79.3% mAP for an input size of 300×300

which is higher than SSD and DSSD by 1.8% and 0.9%

respectively. On the UCAS-AOD remote sensing dataset

the proposed model obtained a 91.0% mAP

which is 2.8% and 1.9% higher than SSD and DSSD

respectively. The testing speed of the model is 21 frame per second on Titan X GPU

which is much faster than DSSD.

Conclusion

2

In this study

a single-stage object detection model using convolution filter pyramid and atrous convolution is proposed. First

feature information is merged through pixel-by-pixel addition and channel concatenation to form a feature map with rich semantic and detailed information. The obtained information was used as a prediction feature map to provide rich feature information in predicting boundary box categories and locations. Then

the convolution filter pyramid structure is introduced into the anchor mechanism to overcome the mismatch between the feature region and corresponding anchor

as well as to accurately detect multiscale objects. At the same time

the atrous convolution is introduced to increase the receptive field of the convolution filter without increasing the number of parameters.The number of anchors is determined according to the generated prediction tensor to reduce the time complexity. The proposed model exhibited a faster detection speed and higher detection accuracy than the current advanced methods

especially in solving the problems of small objects and detecting overlapped objects

due to the effective information fusion and introduction of a convolution filter pyramid structure in the anchor mechanism. Although the proposed method demonstrated a good result in terms of detection speed and accuracy

the detection accuracy of the algorithm can still be further improved compared with the two-stage algorithm because the research on the former is limited in the feature fusion part. In the future

further research will be conducted in the feature fusion part to improve the detection accuracy of the algorithm.