# Research on Improved Lightweight YOLOv5s for Multi-Scale Ship Target Detection
## 2 Related Works
(2020) applied multi-scale detection technology and an SE attention mechanism to improve the YOLOv4-tiny model to integrate multi-level semantic information to achieve real-time detection and early warning of ship fires in complex environments. [PERSON] et al. (2021) adopt the lightweight feature extraction networks IMNet and Slim-bifpn to improve the YOLOv5 model, which enhances the feature extraction capability of the model and reduces the number of parameters to achieve fast and accurate recognition of small-scale ship targets on platforms with limited computational resources. Table 1 lists the main improvement methods and shortcomings of the related work above. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Year** & **Structural Features** & **Advantages and Limitations** \\ \hline [PERSON] et al. (2012) & 2021 & Using Mask R-CNN algorithm and Faster R-CNN algorithm to identify and compare ship targets & The model complexity is high, and the inference speed is slow \\ [PERSON] et al. (2013) & 2019 & Optimizing the Faster R-CNN model using a layered narrowed network & The dataset contains a single type, and the inference speed is slow \\ [PERSON] et al. (2014) & 2021 & Max pooling layers and optimized loss functions & High requirements for GPUs \\ [PERSON] et al. (2015) & 2022 & Optimizing model complexity using multi-objective firefly optimization algorithm & High detection accuracy. \\ [PERSON] et al. (2016) & 2023 & Use MobileNetV3 and CNeB modules to compress model parameters & Slow inference speed on CPU \\ [PERSON] et al. (2017) & 2022 & Using ECIoU loss function; & Relatively large amount of calculation \\ [PERSON] et al. (2018) & 2020 & Fused with depthwise separable convolution & The model is highly complex and relies heavily on the GPU \\ [PERSON] et al. (2018) & 2020 & Using structured pruning methods to compress model complexity & Insufficient attention to multi-scale target detection \\ [PERSON] et al. (2019) & 2022 & GhostNet is adopted for feature detection & Insufficient reliability and robustness of the model \\ [PERSON] et al. (2020) & 2022 & SE attention module is used to extract feature information & Suitable for small target detection \\ [PERSON] et al. (2021) & 2023 & IMNet and Slim-BiFPN networks are used to compress the model calculation amount & The target scene is single and suitable for small target detection \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related research work. Most of the above research used lightweight feature extraction networks to improve the target recognition model or use pruning strategies to improve the model itself in a lightweight way [13; 16]. Although the detection speed of the model has been improved to a certain extent after these methods, the model complexity is still at a high level [14; 15; 16; 17]. Some studies have also added attention mechanisms to the improved model to improve the detection accuracy of the model [19; 20; 21], but it is more suitable for detecting small targets, and the detection accuracy is still at a low level. In order to fully compress the complexity of the model and meet the requirements of ship target detection on hardware platforms with limited computing resources, a lightweight detection model YOLOv5s_ESGW suitable for different scenarios and multi-scale targets was developed.