Object Detection for Agricultural Vehicles: Ensemble Method Based on Hierarchy of Classes

Vision-based object detection is essential for safe and efficient field operation for autonomous agricultural vehicles. However, one of the challenges in transferring state-of-the-art object detectors to the agricultural domain is the limited availability of labeled datasets. This paper seeks to address this challenge by utilizing two object detection models based on YOLOv5, one pre-trained on a large-scale dataset for detecting general classes of objects and one trained to detect a smaller number of agriculture-specific classes. To combine the detections of the models at inference, we propose an ensemble module based on a hierarchical structure of classes. Results show that applying the proposed ensemble module increases mAP@.5 from 0.575 to 0.65 on the test dataset and reduces the misclassification of similar classes detected by different models. Furthermore, by translating detections from base classes to a higher level in the class hierarchy, we can increase the overall mAP@.5 to 0.701 at the cost of reducing class granularity.

Automated summary

Vision-based object detection is crucial for autonomous agricultural vehicles, but the limited availability of labeled datasets in the agricultural domain poses a challenge. This paper tackles this challenge by proposing two YOLOv5-based object detection models, one pre-trained on a large-scale dataset for general object detection and another trained on a smaller set of agriculture-specific classes. To improve inference, the authors propose an ensemble module based on a hierarchical structure of classes. Experimental results show that the proposed module increases mAP@.5 from 0.575 to 0.65 on the test dataset and reduces misclassification of similar classes detected by different models. Furthermore, by translating detections to a higher level in the class hierarchy, the overall mAP@.5 can be increased to 0.701 at the cost of reduced class granularity.