Smart farms are crucial in modern agriculture, but current object detection algorithms cannot detect chili Phytophthora blight accurately. To solve this, we introduced the YOLOv8-GDCI model, which can detect the disease on leaves, fruits, and stem bifurcations. The model uses RepGFPN for feature fusion, Dysample upsampling for accuracy, CA attention for feature capture, and Inner-MPDIoU loss for small object detection. In addition, we also created a dataset of chili Phytophthora blight on leaves, fruits, and stem bifurcations, and conducted comparative experiments. The results manifest that the YOLOv8-GDCI model demonstrates outstanding performance across a gamut of comprehensive indicators. In comparison with the YOLOv8n model, the YOLOv8-GDCI model demonstrates an improvement of 0.9% in precision, an increase of 1.8% in recall, and a remarkable enhancement of 1.7% in average precision. Although the FPS decreases slightly, it still exceeds the industry standard for real-time object detection (FPS > 60), thus meeting the requirements for real-time detection.

Addressing the issue of incorrect and missed detections caused by the complex types, uneven scales, and small sizes of defect targets in transmission lines, this paper proposes a defect-detection method based on cross-scale feature fusion, PGE-YOLO. Firstly, feature extraction is enriched by replacing the convolutional blocks in the backbone network that need to be cascaded and fused using the Par_C2f network module, which incorporates a parallel network (ParNet). Secondly, a four-layer efficient multi-scale attention (EMA) mechanism is incorporated into the network’s neck to address long and short dependency issues. This enhancement aims to improve global information retention by employing parallel substructures and integrating cross-space feature information. Finally, the paradigm of generalized feature fusion (GFPN) is introduced and reconfigured to develop a novel CE-GFPN. This model effectively integrates shallow feature information with deep feature information to enhance the capability of feature fusion and improve detection performance. Using a real transmission line multi-defect dataset from UAV aerial photography and the CPLID dataset, ablation and comparison experiments with various models demonstrated that our model achieved superior results. Compared to the initial YOLOv8n model, our model increased the detection accuracy by 6.6% and 1.2%, respectively, while ensuring there is no surge in the number of parameters. This ensures that the real-time and accuracy requirements for defect detection in the industry are satisfied.

Insulator defect detection is a critical task in distribution network inspections. To address issues such as low detection accuracy, high model complexity, and large parameter counts caused by the variety of insulator defect types, this study propose a lightweight multi-defect detection network, LMD-YOLO, based on YOLOv8. The network improves the backbone by introducing SCConv module to improve C2f module, which reduces spatial and channel redundancy, lowering both computational complexity and the number of parameters. The SimAM attention mechanism is integrated to suppress irrelevant features and enhance feature extraction capabilities without adding extra parameters. The SIoU loss function is used in place of CIoU to accelerate model convergence and improve detection accuracy. Additionally, this study creates a target detection dataset that encompasses four types of insulators: insulator, absent insulator, broken insulator, and shedding insulator. Experimental results show that LMD-YOLO achieves a 2% higher average accuracy on the insulator dataset compared to YOLOv8n, with a 24.6% reduction in model parameters, offering an effective solution for smart grid inspections.