## 1 Introduction
### High-Resolution Image Object Detection  
As the resolution of cameras increases, traditional object detection datasets such as MS COCO (640 \(\times\) 640 pixels) no longer meet the needs of researchers. Several works already exist regarding high-resolution aerial and remote-sensing images, such as the VisDrone [3] and UAVDT [4] datasets. [PERSON]. [28] cut high-resolution images into small cropped images by uniform cropping and random cropping for object detection to improve small object detection accuracy. Ref. [29] referred to the crowd counting network density map to generate the density distribution of the high-resolution image and then cropped the image according to the density distribution. Compared with uniform cropping, this method was beneficial in reducing the influence of the background. Ref. [30] proposed a learnable downsampling module to enable the network to focus more on the target's surroundings when downsampling the input images, thereby achieving the segmentation of high-resolution images. Since the creation of PANDA, large-scale, long-term, multi-object visual analysis tasks have gradually become a research hotspot. PANDA provides enriched and hierarchical ground truth annotations, including 15,974.6 k bounding boxes, 111.8k fine-grained attribute labels, 12.7 k trajectories, 2.2 k groups, and 2.9 k interactions [5]. The image-cropping method is used chiefly in high-resolution images. However, for gigapixel-level images, this method suffers from a considerable time cost, and the cropped image contains a large number of background and cropped objects. Ref. [31] proposed a novel patch arrangement framework. First, the authors obtained the image's cropping area according to the target's distribution and placed the cropped images on a uniform canvas. Finally, object detection was performed on these canvases. Ref. [32] proposed a region NMS algorithm, a fusion strategy involving the mapping of a small image onto a large image to eliminate the cropped image, resulting in redundant fragmentation boxes. The authors used a sliding window to crop all original images and obtained pre-detection results. Then, they used the labels of the images to crop the objects. However, this method inevitably fragmented the objects, resulting in a decrease in target detection performance. [PERSON] [33] proposed GigaDet, a novel gigapixel-level framework. Based on the spatial sparsity of objects in the PANDA dataset, GigaDet proposed a patch generation network to globally locate areas that may contain objects and determine the appropriate adjustment ratio for each patch. The collected multi-scale plaques were then input into the detector in parallel for accurate and fast detection in a local manner. This idea was similar to image cropping but contained more objects. In order to overcome the significant differences in target pose, scale, and occlusion in the PANDA dataset, [PERSON] [34] proposed PVNet. Firstly, the authors designed a deformable deep residual network as the backbone to enhance the effective perceptual field. Secondly, they adopted a path aggregation feature pyramid network to process the multi-scale features. Finally, the DyHead module was introduced to enhance the scale, space, and task awareness of the detection head and further optimize the classification and positioning of pedestrians and vehicles. Object scale differences depend on spatial location. Furthermore, the problem of narrowing feature extraction is caused by overlapping targets. SARNet [35] used spatial attention to enhance the representation ability and dilated the feature pyramid network to improve the detection accuracy for different scales. This paper used a two-stage object detection method for the PANDA dataset. First, we cropped the original gigapixel-level images with a sliding window. Second, we input the cropped image into the YOLOv5 detector. The cropping method inevitably generated cropped targets that non-maximum suppression (NMS) could not remove.