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.

Anomaly Detection for Agricultural Vehicles Using Autoencoders.

The safe in-field operation of autonomous agricultural vehicles requires detecting all objects that pose a risk of collision. Current vision-based algorithms for object detection and classification are unable to detect unknown classes of objects. In this paper, the problem is posed as anomaly detection instead, where convolutional autoencoders are applied to identify any objects deviating from the normal pattern. Training an autoencoder network to reconstruct normal patterns in agricultural fields makes it possible to detect unknown objects by high reconstruction error. Basic autoencoder (AE), vector-quantized variational autoencoder (VQ-VAE), denoising autoencoder (DAE) and semisupervised autoencoder (SSAE) with a max-margin-inspired loss function are investigated and compared with a baseline object detector based on YOLOv5. Results indicate that SSAE with an area under the curve for precision/recall (PR AUC) of 0.9353 outperforms other autoencoder models and is comparable to an object detector with a PR AUC of 0.9794. Qualitative results show that SSAE is capable of detecting unknown objects, whereas the object detector is unable to do so and fails to identify known classes of objects in specific cases.