Improvement of Yolov8 Object Detection Based on Lightweight Neck Model for Complex Images

With the advancement of target detection technology, the need for accurate detection of complex scenes is becoming increasingly important in various industries. This can not only improve productivity, but also ensure public safety. However, the current mainstream target detection algorithms have some problems in dealing with complex scenes, for example, some detection models are not able to detect in real time, and the accuracy of the model is degraded when facing disturbing factors such as target occlusion, and low-contrast scenes. In order for these problems to be mitigated, this paper proposes a lightweight convolution LDGConv (Lightweight-DepthGhost Convolution), which is utilized to improve the YOLOv8 network model by replacing part of the traditional convolution of the Neck network with this convolution, and improving the bottleneck module in a lightweight way. In addition, we add the Coordinate Attention mechanism to the Neck part. Our proposed model improves mAP50 on the VOC dataset by 1.3% while reducing computation and parameters by 9.8% and 15.3%, respectively, compared to the original model. In the experiments on the steel surface defects dataset NEU-DET, our model overall outperforms the current mainstream detection models. The model is capable of high-precision and low-computational-cost target detection, thus saving labor costs and improving public health and safety and productivity.