DACFA-Det: a domain adaptive calibrated free anchor detection network for agricultural similar pests

Fenmei Wang; Haiying Hu; Qiong Zhou; Rujing wang

doi:10.1117/12.2631167

18 March 2022 DACFA-Det: a domain adaptive calibrated free anchor detection network for agricultural similar pests

Fenmei Wang, Haiying Hu, Qiong Zhou, Rujing wang

Proceedings Volume 12168, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021); 121681T (2022) https://doi.org/10.1117/12.2631167
Event: International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), 2021, Harbin, China

Abstract

Computer vision techniques are an important application for intelligent pest detection. However, it suffers from serious problems and challenges, especially distinguishing the targets of pests with high similarity, small size, and sample unbalance. In this paper, a domain-adaptive-calibrated-free-anchor detection network (DACFA-Det) is proposed, in which a balanced learning mechanism is added to the detection network, dealing with sample imbalance, feature similarity confusion, and center point inaccurate. Our method is evaluated on the re-established similar pest dataset (SPD). The final experimental results show that our method can obtain 44.0% mAP (Average Precision) on the SPD. The testing speed achieve 0.045s per image, meeting the real-time requirement, which proved the effectiveness and efficiency of our method for agricultural similar pest detection.

Citation Download Citation

Fenmei Wang, Haiying Hu, Qiong Zhou, and Rujing wang "DACFA-Det: a domain adaptive calibrated free anchor detection network for agricultural similar pests", Proc. SPIE 12168, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), 121681T (18 March 2022); https://doi.org/10.1117/12.2631167

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KEYWORDS

Agriculture

Calibration

Sensors

Target detection

Visualization

Feature extraction

Image fusion

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