中国远洋鱿鱼捕捞技术比较单一，智能化的鱿鱼捕捞技术对远洋渔业资源统计至关重要。在智能化鱿鱼捕捞作业过程中，以YOLOV3算法检测时，其特征提取网络Darknet53模型参数量较大，模型输出权重存储量较高，降低了检测速度。为此，建立轻量化MobileNetV3网络作为特征提取网络，进行初步有效特征提取，再设计CSP瓶颈层作为逆瓶颈结构，提高特征提取能力；最后通过模型建立网络模型的损失函数。在实验室环境下，使用Squid数据集验证轻量化网络模型的有效性，并对其主干网络与损失函数的性能进行分析。通过训练目标数据集，将轻量型网络模型与YOLOV3网络模型结构做消融试验，验证轻量化网络应用在远洋捕捞技术上的准确性以及实用性。结果显示，轻量化网络结构的性能明显优于YOLOV3网络模型结构，可以大幅度降低参数量，提高检测速度，缩短检测时间，提高检测率，提高了鱿鱼实时检测的工作效率。本研究成果为远洋渔业资源调查提供重要依据。

China's offshore squid fishing technology is relatively simple, while intelligent fishing technology is very important for offshore fishery resources statistics. In the process of intelligent squid fishing, when YOLOV3 algorithm is used for detection, the feature extraction network Darknet53 model has a large amount of parameters, and its output weight storage capacity is high, which reduce the detection speed. Therefore, in this study a lightweight MobileNetV3 network was established as a feature extraction network for preliminary effective feature extraction, and then CSP bottleneck layer was designed as an inverse bottleneck structure to improve the feature extraction ability. Finally, the loss function of network model was established by CIoU model. In the laboratory environment, squid data set was used to verify the effectiveness of lightweight network model, and the performance of its backbone network and loss function were analyzed. Through training the target data set, the lightweight network model and YOLOV3 network model structure were used for ablation experiments to verify the accuracy and practicability of lightweight network application in ocean fishing technology. The results show that the performance of lightweight network structure is obviously better than that of YOLOV3 network model structure; it can greatly reduce the number of parameters, improve the detection speed, shorten the detection time, improve the detection rate and improve the efficiency of squid real-time detection. This study provides an important reference for the investigation of pelagic fishery resources.