Locust Recognition and Detection via Aggregate Channel Features
Title: Locust Recognition and Detection via Aggregate Channel Features
Authors: Dewei Yi (Department of Aeronautical and Automotive Engineering, Loughborough University); Jinya Su (Department of Aeronautical and Automotive Engineering, Loughborough University); Wen-Hua Chen (Department of Aeronautical and Automotive Engineering, Loughborough University);
Year: 2019
Citation: Yi, D., Su, J., Chen, W., (2019). Locust Recognition and Detection via Aggregate Channel Features. UK-RAS19 Conference: “Embedded Intelligence: Enabling & Supporting RAS Technologies” Proceedings, 112-115. doi: 10.31256/UKRAS19.30
Abstract:
Locust plagues are very harmful for food security, quality and quantity of agricultural products. With this consideration, precise locust detection is significant for preventing locust plagues. To achieve this task, aggregate channel feature (ACF) object detector with parameters optimization is applied to detect locusts. Experiment results show that ACF object detector with optimized parameters can achieve 0.39 for average precision and 0.86 for log-average miss rate. Moreover, ACF is a non-deep method using a simple model to detect objects. That is, the proposed method is promising to be embedded in a real-time locust detection system.