# Long-Tailed Metrics and Object Detection in Camera Trap Datasets
###### Abstract
It is common knowledge that species with similar characteristics--particularly those within the same class, family or genus--tend to share similar body parts: for instance, squirrels generally share similar body and tail shapes. Transferring this shared knowledge between images of different species, to mitigate the effects of class imbalance, can help improve long-tailed object detection ability, especially for rare classes. Exploiting the invariant features between images belonging to the same species is also helpful. The diverse and firm sample relationships in camera trap datasets can be utilized to alleviate the issue of insufficient images for rare classes. [PERSON] et al. [26; 27] devised a simple yet effective batch transformer block that enables deep recognition and object detection models to explore sample relationships from the perspective of the batch dimension, facilitating the transfer of knowledge from frequent classes to rare ones. Though they extensively evaluated the effectiveness of this approach on multiple benchmark long-tailed datasets, it has not been tested on any camera trap datasets to date. The distribution of object sizes can also impact the performance of object detection models, particularly with respect to small objects that usually have indistinguishable features and limited context information [28; 29; 30]. [PERSON] et al. [25] observed a skewness in the distribution in favor of small objects in the MS-COCO dataset, and defined certain sizes of objects or input bounding boxes over-represented as an object/box-level scale imbalance. From the perspective of object/box-level scale imbalance, what is the distribution of the animal objects sizes in camera trap datasets? Is the distribution similar to the MS-COCO dataset or the long-tailed dataset, in which certain sizes of animal objects have a significant number of images, while others have very few? Is exploiting sample relationships also effective at improving the detection performance for different sizes of animal objects? Thus far, no research has been conducted to answer these questions. In this work, we focused on the long-tailed metrics and object detection in camera trap datasets. The main contributions of this paper are as follows: (1) We utilized four commonly used metrics to quantitatively analyze the class imbalance in several camera trap datasets, revealing that it was more severe than in the benchmark long-tailed dataset. (2) We also quantitatively analyzed the object/box-level scale imbalance in camera trap datasets, revealing that it too was more severe than in benchmark datasets. Moreover, the camera trap datasets contained too few small objects, making the object detection more challenging. (3) For the first time, we utilized a simple yet effective module, named BatchFormer (Batch transFormer), to explore the effectiveness of sample relationships. The results demonstrated that exploiting sample relationships can improve object detection performance in long-tailed camera trap datasets, in terms of class and object/box-level scale imbalance. The structure of this paper is as follows. In Section 2, we present the Materials and Methods used in this study. The camera trap dataset details, several metrics, and object detection experiments results for multiple camera trap datasets are presented in Section 3. In Section 4, we discuss our findings, and look ahead to future work. In Section 5, we present the conclusions.