Improved YOLOv7-Tiny Complex Environment Citrus Detection Based on Lightweighting

Abstract

In complex citrus orchard environments, light changes, branch shading, and fruit overlapping impact citrus detection accuracy. This paper proposes the citrus detection model YOLO-DCA in complex environments based on the YOLOv7-tiny model. We used depth-separable convolution (DWConv) to replace the ordinary convolution in ELAN, which reduces the number of parameters of the model; we embedded coordinate attention (CA) into the convolution to make it a coordinate attention convolution (CAConv) to replace the ordinary convolution of the neck network convolution; and we used a dynamic detection head to replace the original detection head. We trained and evaluated the test model using a homemade citrus dataset. The model size is 4.5 MB, the number of parameters is 2.1 M, mAP is 96.98%, and the detection time of a single image is 5.9 ms, which is higher than in similar models. In the application test, it has a better detection effect on citrus in occlusion, light transformation, and motion change scenes. The model has the advantages of high detection accuracy, small model space occupation, easy application deployment, and strong robustness, which can help citrus-picking robots and improve their intelligence level.

Lightweight Non-Destructive Detection of Diseased Apples Based on Structural Re-Parameterization Technique

“…Traditional target detection methods, such as sliding windows and manual feature extraction, are exemplified by techniques like Haar [3], HOG [4], Hu moment [5], SIFT [6], SURF [7], and DPM [8]. The evolution of computer vision and deep learning has ushered target detection into agricultural production prominence, with algorithms bifurcated into single-stage (e.g., YOLO series [9][10][11], SSD series [12][13][14], RetinaNet series [15,16]) and two-stage detection algorithms (e.g., RCNN series [17], FasterRCNN series [18]). Apple target detection, melding computer vision and agriculture, automates apple identification and localization in images.…”

Efficient Object Detection and Recognition of Body Welding Studs Based on Improved YOLOv7

“…In recent years, people have begun to widely apply deep learning methods to machine vision target detection in different scenarios, including fruit detection [7][8][9], vehicle detection [10], ship detection [11,12], defect detection [13][14][15], behavior detection [16,17], etc., and have achieved good results. Compared with traditional target detection algorithms, deep convolutional networks can automatically learn multilevel feature models from training data and have strong generalization and feature extraction abilities.…”

Efficient Eye State Detection for Driver Fatigue Monitoring Using Optimized YOLOv7-Tiny

“…This study introduces a real-time driver fatigue detection system utilizing an optimized You Only Look Once version 7-tiny (YOLOv7-tiny) model. YOLOv7-tiny, chosen for its real-time processing capabilities and efficiency, is ideal for in-vehicle systems without high computational demands [21][22][23][24]. This model was selected based on its superior performance in diverse and challenging visual environments, demonstrating robust adaptability to various lighting conditions and head poses.…”