Agriculture pest disaster is one of the most important reasons that reduce grain yield. Accurate recognition and detection are the core of integrated pest management (IPM). Existing deep learning-based methods improve the capacity of feature extraction, but ignore the imbalance of object number and size distribution result in insufficient performance. Therefore, we design a joint balance-distribution oriented composition (JBDOC) to detect multi-class pests with the synthetic dataset. Object bounding boxes and white background boards are used to construct the balanced synthetic dataset for training the convolutional neural network (CNN). Our JBDOC solves the distribution imbalance without methods restriction and improves the test performance without extra time consumption. We combine the JBDOC with current popular detection methods to verify the validity. Experimental results show that the JBDOC greatly improves the performance of deep learning-based detectors in the pest field.
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.
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