The existing ripeness detection algorithm for strawberries suffers from low detection accuracy and high detection error rate. Considering these problems, we propose an improvement method based on YOLOv5, which firstly reconfigures the feature extraction network by replacing ordinary convolution with hybrid depth deformable convolution. In the second step, a double cooperative attention mechanism is constructed to improve the representation of strawberry features in complex environments. Finally, cross-scale feature fusion is proposed to fully integrate the multiscale target features. The method was tested on the strawberry ripeness dataset, the mAP reached 95.6 percentage points, the FPS reached 76, and the model size was 7.44M. The mAP and FPS are 8.4 and 1.3 percentage points higher respectively than the baseline network. The model size is reduced by 6.28M. This method is superior to many state-of-the-art algorithms in terms of detection speed and accuracy. The system can accurately identify the ripeness of strawberries in complex environments, which could provide technical support for automated picking robots.

Insulators are one of the key components in high-voltage power systems that prevent transmission lines from grounding. Since they are exposed to different kinds of harsh environments and climates, periodic inspection is indispensable for the safety and high quality of power grid. Nowadays, Unmanned Aerial Vehicle (UAV) inspection is more widely used, facilitating incorporation of CNN-based detectors in the insulator detection task. However, these methods are generally based on the assumption that the image samples are balanced among different categories and possess completely ideal annotations. The problem of sample imbalance or incomplete annotation is rarely investigated in depth for insulator defect detection. In this paper, we focus on insulator defect detection with imbalanced data and incomplete annotations. Our proposed framework, named Pi-Index, introduces Positive Unlabeled (PU) learning to solve the problem of incomplete annotation and designs a novel index the class prior, which is a key parameter in PU learning. Moreover, focal loss is integrated in our framework to alleviate the effect of sample imbalance. Experiment results demonstrate that the proposed framework achieves better performance than the baseline methods in situations of sample imbalance and missing annotation.