## 1 Introduction  
Birds have various effects on human-related activities. In aviation, birds attacking aircraft will cause serious flight accidents, and it is very important to detect all the surrounding birds before they strike the aircraft. In addition, if we know the activity information of birds, we can not only prevent the mass death of birds in terms of an ecological standpoint but also prevent the crops from being damaged due to birds in terms of agriculture. In this paper, we specifically focus on detecting the presence of all infrared birds in the images. If all birds can be accurately detected in the monitoring area, various bird-related issues can be solved. Detection of flying birds from video sequences has gained a lot of interest in recent years due to its variety of applications such as aviation safety, avian protection, and the ecological science of migrant bird species [1, 2, 3, 4]. However, there are still great challenges in detecting infrared birds in the outdoor environment due to low signal-to-noise ratio, small target size, illumination variation, shadows, occlusion, shape deformation, etc., so it is of great significance to study efficient bird target detection and location methods. In recent decades, researchers have conducted numerous studies on infrared dim target detection algorithms for image sequences, which mainly include traditional image processing techniques and deep learning methods. Traditional infrared small target detection methods are mainly divided into two categories: spatial domain detection methods and time-domain detection methods. Spatial domain detection methods [5, 6, 7, 8, 9, 10, 11] normally utilized the contrast between the targets and the surrounding background to distinguish the small targets from the background. For a simple background, these methods can effectively suppress clutter and enhance the target. However, when a large number of noises and clutters are similar to the target, these algorithms will face problems such as a low detection rate, a high false alarm rate, poor real-time performance, etc. The time domain detection methods [12; 13] mainly use the strong correlation between adjacent image frames to estimate the background so as to calculate the difference between the observed image and the background to obtain the target. Because additional image temporal information is used, these detection methods generally have a higher detection accuracy, however, when non-target objects such as clouds and trees move in front of the camera or the camera moves in the environment, the target detection performance will be greatly reduced. With the rapid development of deep convolutional neural networks (CNN [14]), CNN-based object detection methods have achieved significant performance improvements in object detection tasks. Target detection methods based on CNN [14] mainly include two-stage target detection methods and one-stage target detection methods. The two-stage target detection methods first generate candidate regions, and then classify and regress the candidate regions, which mainly include R-CNN [15], Fast R-CNN [16], Faster R-CNN [17], Mask-R-CNN [18], and Cascade R-CNN [19], etc. The one-stage target detection methods such as Single Shot MultiBox Detector (SSD [20]), YOLO [21], YOLO9000 [22], YOLOv3 [23], YOLOv4 [24], RFB-Net [25], and RefineDet [26] do not generate candidate regions but directly perform classification and regression, which have better real-time performance than the two-stage methods. The single shot multibox detector (SSD [20]) is one of the fastest algorithms in the current target detection field. It has achieved good results in target detection, but there are problems such as poor extraction of features in shallow layers and loss of features in deep layers [27]. By applying a context information scene perception (CISP) module to the network structure of SSD, CISPNet [28] is proposed to detect small targets with a higher detection accuracy than the original SSD [20].