Improved YOLOv7 Algorithm for Low-resolution Ship Object Detection in Complex Backgrounds#br#

doi:10.3969/j.issn.1006-2475.2023.11.019

Abstract

Abstract: Abstract: In response to the problems of low resolution target detection and interference from complex backgrounds in ship image target detection， an improved YOLOv7 algorithm is proposed for identifying ship targets. The algorithm is mainly improved in three aspects: using K-means++ algorithm for anchor box clustering in the ship target dataset to obtain anchor box information that is more suitable for ship detection tasks; improving the loss function by using EIOU loss instead of CIOU loss and using Focal loss combined with ɑ-Balanced instead of standard cross-entropy loss; improving the network structure by adding the SPD-Conv module to enhance the detection effect for low-resolution targets. Experimental results show that compared with the original YOLOv7 algorithm， the improved algorithm has an accuracy improvement of 4.22 percentage points， a recall rate improvement of 2.68 percentage points， a mAP@0.5 improvement of 4.3 percentage points， and a detection speed improvement of 2 frames/s. The algorithm achieves good detection results for ship targets.

Key words: Key words: object detection, ship detection, YOLOv7, complex background, low resolution

CLC Number:

TP391

YAN Zi-xian, DONG Bao-liang, TANG Si-mi. Improved YOLOv7 Algorithm for Low-resolution Ship Object Detection in Complex Backgrounds#br#[J]. Computer and Modernization, 2023, 0(11): 120-126.

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