## 2 Materials and Methods
### Scientific Problems for Gigapixel Images  
This paper is based on the gigadetection track of the gigavision competition held by Tsinghua University in 2022. The gigadetection track included object detection for pedestrians and vehicles. The training set contained 390 pictures in 13 scenes, and the competition test set contained 165 pictures in 5 scenes; each picture had a resolution of gigapixels and contained a large number of multi-scale objects. Pedestrians and vehicles had different representations and categories. Pedestrians had different postures ('standing', 'walking','sitting', or 'riding') and different ages ('adult' or 'child'). Vehicles had different sizes ('small car','midsize car', or 'large car') and categories ('bicycle','motorcycle', 'tricycle', 'electric car', or 'baby carriage'). The main challenges of this competition were that: (1) The size of the images was much larger than any previous dataset. For the PANDA dataset, the mAP of our method was 61.4 %. In the 31 December 2022 [[https://www.gigavision.cn/competition](https://www.gigavision.cn/competition)]([https://www.gigavision.cn/competition](https://www.gigavision.cn/competition)), our method was placed 10 th. As shown in Figure 0(a), the width and height of the images in the VisDrone dataset are only 1920 pixels and 1080 pixels, respectively, which is one-tenth the size of the PANDA dataset images. This means that PANDA has an image area hundreds of times larger than VisDrone. Existing detectors cannot directly perform object detection on such images. (2) There was a large background area in the image. As shown in Figure 0(b), almost two-thirds of the area had no targets. This meant that an unbalanced number of positive and negative samples would lead to performance damage when we performed object detection on this image. (3) Figure 0(c) depicts the large difference in the target sizes of pedestrians and vehicles, which meant that if the cropping size was too small, the vehicles would be easily cut into pieces, and if it was too large, the pedestrian targets would be too small to detect. Due to these challenges, we made a series of improvements, and the experiments and competition results proved that our improvements were effective. One of the main difficulties in PANDA is that the resolution is too high to use current state-of-the-art object detectors directly. The competition spons provided three baselines to detect gigapixel-level images directly. RetinaNet achieved a 0.225 mAP and a 0.272 F1 score, Faster R-CNN achieved a 0.284 mAP and a 0.331 F1 score, and Cascade R-CNN achieved a 0.309 mAP and a 0.352 F1 score. Currently, several works have been published on object detection in gigapixel-level images. Due to the limitations of GPU memory, the current methods cannot use the whole image as the input of the deep learning model. They usually employ a sliding window strategy to crop and resize the image. After obtaining the detection results of each sub-image, they combine them to obtain the final detection result. The authors of [5] used Faster R-CNN with the above cropping scheme and obtained a 75.5% AP.50 for the visible part of the large object. However, in the same setting, the AP.50 for small objects was only 20.1%, which was unsatisfactory. It is also a challenge to select an appropriate cropping window size. A large cropping window leads to the inaccurate detection of small objects. Similarly, a small cropping window causes large objects to be cropped, affecting performance. This study explored the performance of existing object detection methods in a gigapixel-level dataset. We used the single-stage detector YOLOv5 as a baseline. Due to the higher accuracy for small targets, TPH-YOLOv5 [36] performed better than YOLOv5 for the remote-sensing dataset UAVDT. However, compared to YOLOv5, TPH-YOLOv5 adds additional computational overhead. Although there are already better YOLO algorithms, such as YOLOv7 [37], considering the calculation resources and accuracy trade-off, we still used YOLOv5 as a baseline.