Foreign objects such as balloons and nests often lead to widespread power outages by coming into contact with transmission lines. The manual detection of these is labor-intensive work. Automatic foreign object detection on transmission lines is a crucial task for power safety and is becoming the mainstream method, but the lack of datasets is a restriction. In this paper, we propose an advanced model termed YOLOv8 Network with Bidirectional Feature Pyramid Network (YOLOv8_BiFPN) to detect foreign objects on power transmission lines. Firstly, we add a weighted cross-scale connection structure to the detection head of the YOLOv8 network. The structure is bidirectional. It provides interaction between low-level and high-level features, and allows information to spread across feature maps of different scales. Secondly, in comparison to the traditional concatenation and shortcut operations, our method integrates information between different scale features through weighted settings. Moreover, we created a dataset of Foreign Object detection on Transmission Lines from a Drone-view (FOTL_Drone). It consists of 1495 annotated images with six types of foreign object. To our knowledge, FOTL_Drone stands out as the most comprehensive dataset in the field of foreign object detection on transmission lines, which encompasses a wide array of geographic features and diverse types of foreign object. Experimental results showcase that YOLOv8_BiFPN achieves an average precision of 90.2% and an mAP@.50 of 0.896 across various categories of foreign objects, surpassing other models.

Foreign object detection on transmission lines poses a significant threat to power grid security, while conventional manual inspection methods are inefficient and pose safety risks. To overcome the challenges of detecting foreign objects in complex environments, this paper proposes an enhanced YOLOv11_SDI detection framework with two key contributions. Firstly, a novel hierarchical Spatial-channel Dynamic Inference (SDI) module is integrated into YOLOv11, employing an adaptive feature fusion mechanism to enhance multi-scale representation. Secondly, a lightweight spatial attention unit is introduced to improve region-of-interest localization without compromising computational efficiency. In addition, the publicly available FOTL_Drone dataset is expanded to 5980 UAV images through systematic data augmentation, covering six critical foreign object categories. Comprehensive experiments validate the model’s superior performance, achieving state-of-the-art 95.2% mAP@0.50 with only 3.74 M parameters, demonstrating its potential for practical transmission line inspection applications.

With the rapid development of document digitization, people have become accustomed to capturing and processing documents using electronic devices such as smartphones. However, the captured document images often suffer from issues like shadows and noise due to environmental factors, which can affect their readability. To improve the quality of captured document images, researchers have proposed a series of models or frameworks and applied them in distinct scenarios such as image enhancement, and document information extraction. In this paper, we primarily focus on shadow removal methods and open-source datasets. We concentrate on recent advancements in this area, first organizing and analyzing nine available datasets. Then, the methods are categorized into conventional methods and neural network-based methods. Conventional methods use manually designed features and include shadow map-based approaches and illumination-based approaches. Neural network-based methods automatically generate features from data and are divided into single-stage approaches and multi-stage approaches. We detail representative algorithms and briefly describe some typical techniques. Finally, we analyze and discuss experimental results, identifying the limitations of datasets and methods. Future research directions are discussed, and nine suggestions for shadow removal from document images are proposed. To our knowledge, this is the first survey of shadow removal methods and related datasets from document images.