Wenxian Zeng
ABSTRACT
In many applications, existing image detection algorithms detect small targets in complex scenes with low recognition rate and biased classification. To address this issue, we develop a multi-target detection algorithm based on improved Retinanet. It consists of the following four parts: 1) extract rich texture features using the information interaction module; and 2) extract high-level abstract features through the improved FPN+ module; and 3) make full use of contextual information for detection leveraging SSH detection head; and 4) adopt the weighted loss for regression. For the accuracy of large and small categories and avoid bias, we employ the ensemble of loss function to optimize parameters. The experimental results confirm the feasibility of our detection algorithm, with the improved performance on four metrics. The mAP50 value and mmAP value increase to 96.75% and 75.08% respectively, and the ACD value decreases by 0.907.
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