Drone-based object detection has been widely applied in ground object surveillance, urban patrol, and some other fields. However, the dramatic scale changes and complex backgrounds of drone images usually result in weak feature representation of small objects, which makes it challenging to achieve high-precision object detection. Aiming to improve small objects detection, this paper proposes a novel cross-scale knowledge distillation (CSKD) method, which enhances the features of small objects in a manner similar to image enlargement, so it is termed as ZoomInNet. First, based on an efficient feature pyramid network structure, the teacher and student network are trained with images in different scales to introduce the cross-scale feature. Then, the proposed layer adaption (LA) and feature level alignment (FA) mechanisms are applied to align the feature size of the two models. After that, the adaptive key distillation point (AKDP) algorithm is used to get the crucial positions in feature maps that need knowledge distillation. Finally, the position-aware L2 loss is used to measure the difference between feature maps from cross-scale models, realizing the cross-scale information compression in a single model. Experiments on the challenging Visdrone2018 dataset show that the proposed method draws on the advantages of the image pyramid methods, while avoids the large calculation of them and significantly improves the detection accuracy of small objects. Simultaneously, the comparison with mainstream methods proves that our method has the best performance in small object detection.

The majority of modern object detectors rely on a set of pre-defined anchor boxes, which enhances detection performance dramatically. Nevertheless, the pre-defined anchor strategy suffers some drawbacks, especially the complex hyper-parameters of anchors, seriously affecting detection performance. In this paper, we propose a feature-guided anchor generation method named dynamic anchor. Dynamic anchor mainly includes two structures: the anchor generator and the feature enhancement module. The anchor generator leverages semantic features to predict optimized anchor shapes at the locations where the objects are likely to exist in the feature maps; by converting the predicted shape maps into location offsets, the feature enhancement module uses the high-quality anchors to improve detection performance. Compared with the hand-designed anchor scheme, dynamic anchor discards all pre-defined boxes and avoids complex hyper-parameters. In addition, only one anchor box is predicted for each location, which dramatically reduces calculation. With ResNet-50 and ResNet-101 as the backbone of the one-stage detector RetinaNet, dynamic anchor achieved 2.1 AP and 1.0 AP gains, respectively. The proposed dynamic anchor strategy can be easily integrated into the anchor-based detectors to replace the traditional pre-defined anchor scheme.

Display crosstalk defect detection is an important link in the display quality inspection process. We propose a crosstalk defect detection method based on salient color channel frequency domain filtering. Firstly, the salient color channel in RGBY is selected by the maximum relative entropy criterion, and the color quaternion matrix of the displayed image is formed with the Lab color space. Secondly, the image color quaternion matrix is converted into the logarithmic spectrum in the frequency domain through the hyper-complex Fourier transform. Finally, Gaussian threshold band-pass filtering and hyper-complex inverse Fourier transform are used to separate the low-contrast defects and background of the display image. The experimental results show that the accuracy of the proposed algorithm reaches 96% for a variety of crosstalk defect detection. Compared with the current advanced defect detection algorithms, the effectiveness of the proposed method for low-contrast crosstalk defect detection is confirmed.