Lightweight underwater object detection method

A lightweight underwater object detection method, comprising:

• S1:
• using YOLOv8 as a baseline model, implementing feature reuse in a network backbone of the YOLOv8 using a lightweight RepGhost re-parameterization module, and replacing a convolution layer of the network backbone with an SPD convolution module;
• S2:
• replacing a network neck of the YOLOv8 with a global feature pyramid network (GFPN) for feature fusion;
• S3:
• replacing a network head of the YOLOv8 with a CLLA detection head to improve recognition and positioning of an object, thereby obtaining a lightweight underwater object detection model;
• S4:
• performing performance evaluation on the lightweight underwater object detection model, and applying the lightweight underwater object detection model to a real-world scenario to realize underwater object detection, wherein
• the CLLA detection head in S3 comprises a CLLA cross-layer local attention mechanism layer and a DFL loss function layer, and the CLLA cross-layer local attention mechanism layer and the DFL loss function layer are connected via the convolution layer;
• the CLLA cross-layer local attention mechanism layer performs operations including:
• S31:
• assuming that a low-level feature map
• F
• 1
• ′
• and a mid-level feature map
• F
• 2
• ′
• contain shallow information, and a high-level feature map
• F
• 3
• ′
• contains deep semantic information;
• S32:
• respectively reducing a spatial size of the low-level feature map
• F
• 1
• ′
• and a spatial size of the mid-level feature map
• F
• 2
• ′
• by using average pooling and 1×1 convolution, and unifying a count of channels, to obtain a new low-level feature map
• F
• 1
• l
• and a new mid-level feature map
• F
• 2
• m
• ,
• such that the spatial size of the new low-level feature map
• F
• 1
• l
• and the spatial size of the new mid-level feature map
• F
• 2
• m
• are the same as the spatial size high-level feature map
• F
• 3
• ′
• ;
• S33:
• using learnable parameters to project the new low-level feature map
• F
• 1
• 1
• into a space of Q, and to project the new mid-level feature map
• F
• 2
• m
• into spaces of K and V respectively, with formulas as follows:
• Q
• =
• F
• 1
• l
• ⁢
• W
• Q
• ;
• K
• =
• F
• 2
• m
• ⁢
• W
• K
• ;
• V
• =
• F
• 2
• m
• ⁢
• W
• V
• ;
• where Q, K, and V represent a query vector, a key vector, and a value vector respectively, and WQ, WK, and WV denote three learnable parameters used to generate the query vector, the key vector, and the value vector respectively;
• S34:
• calculating a correlation weight between the Q and the K by using a dot product and a softmax function, and then performing a dot product between the correlation weight and the V to obtain a new feature map;
• S35:
• adding the new feature map to the high-level feature map
• F
• 3
• ′
• to obtain an aggregated feature map FM, with a formula as follows:
• F
• M
• =
• F
• 3
• ′
• +
• softmax
• ⁢
• (
• (
• F
• 1
• l
• ⁢
• W
• Q
• )
• ⁢
• (
• F
• 2
• m
• ⁢
• W
• K
• )
• T
• d
• k
• )
• ⁢
• (
• F
• 2
• m
• ⁢
• W
• V
• )
• ;
• where softmax(⋅) denotes an activation function, T denotes a matrix transpose, and dk denotes a vector dimension used for calculating an attention weight;
• the DFL loss function layer uses a cross-entropy function to focus a network distribution near a label value, with a formula as follows:
• DFL
• (
• S
• i
• ,
• S
• i
• +
• 1
• )
• =
• -
• (
• (
• P
• i
• +
• 1
• -
• P
• )
• ⁢
• log
• ⁡
• (
• S
• i
• )
• +
• (
• P
• -
• P
• i
• )
• ⁢
• log
• ⁡
• (
• S
• i
• +
• 1
• )
• )
• ;
• where DFL(si,si+1) denotes a distributed focus loss of a predicted value at two adjacent positions i and i+1, Si denotes an output of a network Sigmoid at the position i, Si+1 denotes an output of the network Sigmoid at the position i+1, Pi denotes a probability of a label P at the position i, and Pi+1 denotes a probability of the label P at the position i+1.