# Object Detection Based on Multiple Information Fusion Net
However, the multiple scale proposals generated by sliding a fixed set of filters over a fixed set of convolutional feature maps in RPN may be inconsistent with the sizes of objects. Thus, [PERSON] et al. proposed a multi-scale CNN (MS-CNN) [18] to match the receptive fields to different scales of objects and employed a multiple output layer for object detection. Recently, for the purpose of improving the detection performance, some more state-of-the-art techniques (such as Resnet [19] and Inception series [20; 21; 22]) were employed to replace the standard CNN as the backbone networks of the two-stage based object detection methods, which can be found in the object detection API from google [23]. Different from the aforementioned methods, the one-stage approaches can achieve complete single network training under the premise of guaranteeing a certain accuracy rate. You only look once (YOLO) [24], YOLO9000 [25], an iterative grid based object detector (G-CNN) [26], and single shot multibox detector) [27] are representative techniques in this category. Through treating the object detection task as a regression problem, YOLO spatially separates bounding boxes and associated class probabilities. Since the whole detection pipeline of YOLO is a single network, an end-to-end optimization of the network can be directly performed. SSD combines predictions of multiple feature maps with different resolutions to detect objects of various sizes. Since the proposal generation, subsequent pixel and feature resampling stages are eliminated in SSD, it can be easily trained. Although the running speed of one-stage methods can be significantly improved, their accuracy is always inferior to the two-stage approaches [27]. To address this issue, the Resnet and Inception have also been utilized [23]. Furthermore, [PERSON] et al. replaced the standard cross entropy loss with a novel Focal loss and proposed a RetinaNet to solve the class imbalance problem in one-stage based object detection [28]. No matter the approach belongs to one-stage or two-stage, most the aforementioned algorithms do not effectively utilize the relationship among objects, but only use the feature associated with the object itself for detection. Recently, some researchers have realized the importance of relation, and proposed some methods [29; 30; 31] to achieve better detection results by exploring the relationships between objects. In ION [32], [PERSON] et al. proposed a spatial recurrent neural networks (RNNs) for exploring contextual information across the entire image. [PERSON] et al. put forward a scene graph generation approach by iterative message passing [33]. The network regards a single object as a point in topology, and the relationships of objects are considered as edges connecting points. Through passing information between the edges and points, it is proved that the relationship between objects has a positive impact on detection. [PERSON] et al. proposed a human-centric based model called InteractNet [34], in which human is regarded as the main clue to establish a relationship with other surrounding objects. The InteractNet indicates that a person's external behavior can provide powerful information to locate the objects they are interacting with. [PERSON] et al. proposed a structure inference net (SIN) [35] which explores the structure relationship between objects for detection. However, SIN only takes the spatial coordinates of object proposals into account, while the appearance feature of object proposals is neglect. [PERSON] et al. presented a relation network [36], which considers both the appearance and geometry feature of object proposals for relation construction. Nevertheless, the scene-level feature which could provide a lot of context information for object detection [37] is ignored in relation Nntwork. This paper proposes a novel object detection algorithm based on multiple information fusion net (MIFNet).