Object Detection Based on YOLOv5 and GhostNet for Orchard Pests

Abstract

Real-time detection and identification of orchard pests is related to the economy of the orchard industry. Using lab picture collections and pictures from web crawling, a dataset of common pests in orchards has been created. It contains 24,748 color images and covers seven types of orchard pests. Based on this dataset, this paper combines YOLOv5 and GhostNet and explains the benefits of this method using feature maps, heatmaps and loss curve. The results show that the mAP of the proposed method increases by 1.5% compared to the original YOLOv5, with 2× or 3× fewer parameters, less GFLOPs and the same or less detection time. Considering the fewer parameters of the Ghost convolution, our new method can reach a higher mAP with the same epochs. Smaller neural networks are more feasible to deploy on FPGAs and other embedding devices which have limited memory. This research provides a method to deploy the algorithm on embedding devices.

Electrical insulator defect detection with incomplete annotations and imbalanced samples

“…The proposed framework introduced an oriented RPN to implement arbitrarily oriented localization for insulators. In [32], the attention mechanism was introduced in Faster RCNN for self-explosion insulator defects. In detail, an adaptive receptive field network is proposed and inserted into the FPN backbone.…”

“…Our proposed framework is a positive-unlabelled (PU) framework, which is viewed as a combination of a positive-negative (PN) pipeline and PU loss. Therefore, we first selected existing mainstream positivenegative (PN) learning object detection algorithms [31,32] to ablate the influence of PU loss. Furthermore, we also introduced several PU-based detectors [34,49] as contrast methods.…”

“…Moreover, Zhong et al modified the standard Faster RCNN pipeline to consider arbitrarily oriented insulator localization [31]. In [32], the attention mechanism was introduced in Faster RCNN for self-explosion insulator defects. In summary, the aforementioned approaches focused on the following problems:…”

“…modified the standard Faster RCNN pipeline to consider arbitrarily oriented insulator localization [31]. In [32], the attention mechanism was introduced in Faster RCNN for self‐explosion insulator defects. In summary, the aforementioned approaches focused on the following problems: (1) a complex background and various types of insulators make insulator defect detection difficult; (2) some severely damaged insulators are too small to recognize; (3) detection speed and accuracy needs to be balanced.…”

TransLearn-YOLOx: Improved-YOLO with Transfer Learning for Fast and Accurate Multiclass UAV Detection

“…Similarly, the authors used Ghost convolution in the YOLOv5 baseline model and achieved 19.89% less precision than GAANet [24]. YOLOv5x-ALL- GHOST added GhostNet in both head and backbone and got 0.6% less map@0.5 than GAANet [26]. GhostNet feature extraction networking was embedded in YOLOv3 [27] that achieved 8.3% less map@0.5 than GAANet.…”