# Object Detection Using Multi-Scale Balanced Sampling
## 1 Introduction  
Object detection is a kind of approaches for objects localization and category classification in digital images, which is one of the most challenging branches in the field of computer vision.  
The early approaches are based on handcrafted image features. In [1], a pedestrian detection system is proposed with histogram of oriented gradients(HOG) feature and support vector machine(SVM). In [2], deformable part-based model(DPM) is proposed which enhanced detection accuracy by utilizing HOG features of the whole and part of objects. As the peak of handcraft feature based detection approach, the detection performances of DPM are still not ideal, due to the lack of effective representation of features. Besides, since hand craft feature extractor are always designed for specific object types, hence often result in low robustness in dealing with different category of objects.  
In recent years, several object detection approaches based on convolutional neural networks (CNN) are proposed [3; 4; 5]. Image features are supervise trained by measuring error between prediction and annotated ground-truth in large-scale object detection datasets. Compare with handcraft features the CNN features' representation ability and robustness against various types of objects are both significantly enhanced. Therefore the detection performances are highly improved. Although deep learning object detection approaches have shown state of the art performance for general object detection, they are still limited in detecting small objects and the performances in detection various scale objects in single input image is also not ideal. The reasons for a low detection performances are as the follow:1. The proportion of small objects in the image are always relative low, which means they might be excluded from the training process due to improper network hyperparameter settings. However, small objects often have low resolutions and less image information which means the difficulty of training small objects are always higher than general objects, the lack of sampling will further result in a low quality features extraction by the deep neural networks.
2. In natural scenes, the scale of objects are distributed stochastic, which means within a single image there might be objects with large scale variants, it is easy to take the majority of samples and ignore other objects in the process of training.  
Overall, in order to further improve detection performances especially in detecting objects mentioned above, the number of small objects and proportion of various scale object in training samples are both important.In this paper an end-to-end object detection approach with multi-scale balanced sampling is proposed to improve the matching mechanism and ensure scale diversity in training samples. The key contribution of the approach is summarized as follow:  
1. The samples' matching conditions is adjusted according to the objects' scale so that the the small objects are easier to be matched, which enhance the training samples of small objects.
2. The sample set is divided into multiple intervals according to the samples' scale and their corresponding discrimination difficulty. In addition, each interval is sampled in a balanced fashion to preserve the diversity of sample types during the classification and positioning network training process and to further ensure that the algorithm does not tend to detect a specific type of samples while ignoring the others.
3. Evaluation between proposed approach and others on several benchmarks is proposed, the experiment results show that the detection performances are better than other similar approaches.  
The remainder of this paper is organized as follows. In Section 2, background and related works are introduced. In Section 3, framework and implement detail of proposed approach are introduced. Section 4 presents the experiment results and comparisons with other similar approaches. Finally, Section 5 conclude the proposed approach.