# Object Detection in Drone Imagery via Sample Balance Strategies and Local Feature Enhancement
## 3 Methods
### Weight-GAN Sub-Network  
Due to the high frequency and noise surrounding the small objects and occluded objects in the images captured by drones, the detection task is extraordinarily challenging. In this paper, we propose a new GAN with center point weights to locally enhance the feature map, which is computed in a forward propagation of feature extracting modules. We need to train a generator model to generate the super-resolved representations for the small or occluded objects, and design a discriminator model that considers adversarial loss and center-ness loss to differentiate and supervise the generator model training. First, we create a sub-dataset for the GAN task based on Visdrone datasets. This will be used to generate super-resolved large object-like representations for small objects. The samples of the datasets are shown as below in Figure 6. The high resolution objects are cropped from the whole images as raw samples, according to labeled bounding boxes. The sub-datasets contain the object categories of vehicles and people. The Perceptual GAN [46] method provides an idea for detecting small objects by training a GAN model to transfer poor representations of small objects to super-resolved targets that are not easily distinguished by a discriminator. However, the detector based on the Perceptual GAN does not perform well with occluded objects. Due to this, the representation of targets is enhanced using Perceptual GANs, while the occluded objects will be ignored and their representation suppressed. So we proposed the use of center-ness weights to enhance the representation of the center points and suppress the representation of edges for the targets. Inspired by the Perceptual GANs, the sub-net can be described as Figure 7. (a) shows the network generator, which contains a deep residual network to enhance the features from the feature extraction module. In  
Figure 6: The sub-datasets we created for the Generative Adversarial Network (GAN) task to generate high resolution objects and down-sample images. ness weight aims to suppress the features of the edges. (b) shows the framework for the supervision and differentiates whether the image has high resolution target features or super-resolved features. We use super-resolved generated GAN module with centerness weights to enhance local features and suppress the edge part features. Thus the occluded objects can be distinguished effectively by following classification and regression branches, just shown as Figure 8.  
Our model can be formulated as below. Here, \(G\) represents a generator that is trained to map the features data from noisy data to the super-resolved features, and \(D\) represents a discriminator that estimates the probability of a target feature coming from high resolution target features, rather than from \(G\). In this paper, the functions \(F_{l}\) and \(F_{s}\) are the representation for high resolution object features and down-sampling features respectively. The function \(f\) is the generator which learns to generate the residual representation between the representations of high resolution features and low resolution features through residual learning instead. \[ \begin{split}\min_{G}\max_{D}L(D,G)\triangleq E[\log D(F_{l})] \\ +E[\log\left(1-D(\omega_{centerness}F_{s}+G(\omega_{centerness}F_{s} |f))\right)]\end{split}  \tag{1}\]  
Figure 8: Occluded objects feature map visualization of FCOS and weight-GAN method. Figure 7: Details of the training procedure for the weight-GAN sub-network that uses super-resolved feature generation and discrimination. (**a**) shows the network generator, which contains a deep residual network to enhance the features from the feature extraction module. In the meantime, the center-ness weight aims to suppress the features of the edges. (**b**) shows the framework for the supervision and differentiates whether the image has high resolution target features or super-resolve features. In our case, the variant \(\omega_{centness}\) represents the center-ness weight, which can be formulated as below.