## 1 Introduction  
Object instance segmentation [1] is one of the most challenging tasks in computer vision, which needs to locate the object position in the image, classify it, and segment the pixels accurately [2]. The instance segmentation technology can be applied in many fields. For example, in industrial robotics, instance segmentation algorithms can detect and segment parts in different backgrounds, improve the efficiency of automatic assembly and reduce labor cost. Moreover, it can be used for tumor image segmentation, to carry diagnosis and for other aspects to assist the treatment of diseases in terms of intelligent medicine. Especially in autonomous driving, instance segmentation technology can be applied to the perception system of automatic driving vehicles to detect and segment pedestrians, cars and objects in the driving environment, and it can provide data support for the decision-making of automatic driving vehicles. Furthermore, the instance segmentation methods can accomplish the segmentation of obstacles in the image captured by the vehicle camera so as to facilitate the subsequent estimation of its trajectory and ensure safe driving [3]. At present, the instance segmentation methods based on a convolutional neural network (CNN) are mainly divided into two categories, namely, one-stage instance segmentation methods and two-stage instance segmentation methods [4]. The two-stage instance segmentation methods contain two ideas: detection followed by segmentation [5; 6; 7; 8] and embedding cluster [9; 10; 11]. Two-stage instance segmentation methods which are based on the principle of detecting then segmentation first exploit object detection algorithms to find the bounding box of the instance, and then perform semantic segmentation algorithms in the detection box and output the segmentation results as different instances. Two-stage instance segmentation methods with the foundation principle of embedding cluster perform semantic segmentation at a pixel level in images, and then different instances are distinguished by clustering and metric learning. The average precision of the instance segmentation methods on account of two steps is not satisfactory when segmenting crowded, occluded as well as irregular objects, and the speed of generating low resolution mask is not ideal. There are also two categories of one-stage instance segmentation methods according to different solutions. One is inspired by the one-stage anchor-based object detection algorithms, forgoing the sequential execution steps of two-stage instance segmentation methods and making the network learn to locate the instance mask through a related parallel design [12]. The other is aroused by one-stage anchor-free object detection methods which get rid of the limitation of anchor location and scale in structure, and rely on a dense prediction network to achieve precision object detection and segmentation [13]. One-stage instance segmentation methods have more advantages in inference time and need to be further improved in accuracy. Generally, two-stage instance segmentation methods can achieve slightly higher accuracy compared with the one-stage instance segmentation methods, but the inference speed of mask generation is slower. It is difficult for single-layer feature maps, whether high-level or low-level, to cope well with the scale change of instance objects and the imbalance of category data. Therefore, multi-level feature networks are more and more widely used in instance segmentation algorithms to meet the challenge [14]. By fusing the detailed location information of low-level features and rich semantic information of high-level features [15], multi-level feature networks can enhance the representation capacity of features and provide more abundant and beneficial information for detection and segmentation.