# Object Detection in Drone Imagery via Sample Balance Strategies and Local Feature Enhancement
## 3 Methods
### Adaptive Selection for Positive and Negative, and Easy and Hard Samples  
In this paper, we study the relationship and trends between the hyper-parameter \(k\) and the IoU threshold, as shown in Figure 4. We observe hyper-parameter \(k\)'s curve, and the mean and standard deviation of the top \(k\) candidates, and find that when the hyper-parameter \(k\) is set to no less than 9, the IoU's lowest threshold is usually 0. Thus, with a hyper-parameter of 9, the selected positive and negative samples are more balanced, and there is a high probability that it can obtain a higher AP performance. This is consistent with the conclusions that are presented in ATSS [40] with regards to hyper-parameter \(k\). Furthermore, the sample imbalance problem can be partly solved by using the balance strategies on the positive and negative samples balancing, and easy and hard samples balancing. Figure 3: The details of detector head. (**a**) The detailed structure of Offset-Head (regression branch), and (**b**) The definition of four variants. We propose the object detection framework based on FCOS, which utilizes IoU to divide the candidates into positive and negative samples. In the training process, sampler labels the anchor boxes with \(IoU>threshold\) as positives and \(IoU<threshold\) as negatives. In this paper, we use the mean of top \(k\) candidates' IoU as the threshold to make adaptive sample selection, which is sorted by distance from the center point of ground truth. The standard deviation of top \(k\) candidates' IoU can reflect the offsets of the candidated boxes. As shown in Figure 4, when the amount of candidates is creasing, the mean and standard deviation (std) of IoU tend to be constant. Since the distribution of IoU is not normalization, the anchor boxes with \(IoU>mean+std\) belongs to easy-positives. The anchor boxes with \(IoU<mean-std\) belongs to easy-negative. Moreover, the anchor boxes between \(mean-std\) and \(mean+std\) belongs to hard-positive and hard-negative. As shown in Figure 5, according to the statistics of candidates' IoU, we counted the proportion of the amount of IoU values in different intervals. The proportion of hard-samples is higher, so the effective suppression is needed. Positive and negative sample balance strategies: In a picture, the detection target only takes up a small part, and the remaining parts form up the background. During the training process, the boxes with an IoU over 0.5 are usually picked as positive samples, and the target boxes with an IoU below 0.5 are usually detected as negative samples. This will inevitably result in far more negative samples than positive samples, with the training process generally controlling the ratio of positive to negative samples at 1:3 [39]. Easy and hard sample balance strategies: The samples can be divided into four categories: Easy-positive samples, easy-negative samples, hard-positive samples and hard  
Figure 4: The hyper-parameter \(k\)’s curve with the mean and standard deviation of the top \(k\) candidates. Figure 5: The proportion statistics of the amount in different IoU intervals. negative samples. The hard samples mean that they have been incorrectly classified as opposite samples. In general, hard samples contribute a significant loss but a small amount, while easy samples contribute a slight loss but a large amount. Therefore, the loss is impacted more by the easy samples and learned less from hard samples. Following this, we propose strategies on the adaptive selection for positive and negative, and easy and hard samples (ASPNEH) which are based on the idea of adaptive training sample selection (ATSS).